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A Survey On Security and Privacy Aspects and Solutions for Federated Learning in Mobile Communication Networks

Year 2024, Volume: 1 Issue: 1, 29 - 40, 30.09.2024

Şükrü Erdal , Ferhat Karakoç , Enver Özdemir

Abstract

In this study, we delve into cutting-edge solutions for security-centric, privacy-enhanced federated learning, a rapidly evolving area of research that bridges the gap between data privacy and collaborative machine learning. Our analysis offers a comprehensive comparative evaluation of existing methodologies, shedding light on the strengths and limitations of current approaches. By introducing new perspectives, we aim to push the boundaries of secure federated learning, exploring techniques that enhance data protection without compromising learning efficiency. Additionally, we highlight emerging challenges and opportunities in the field, emphasizing the importance of scalable, privacy-preserving mechanisms in decentralized systems. As federated learning continues to gain traction across various sectors such as healthcare, finance, and IoT, our study serves as a foundation for future research, identifying key areas for innovation and improvement. This forward-looking approach ensures that federated learning can continue to evolve as a trustworthy and robust solution for privacy-sensitive applications, addressing both current and future security concerns.

Keywords

Federated learning privacy secure aggregation homomorphic encryption secure multi-party computation

Supporting Institution

TUBITAK

Project Number

5169902

Thanks

This work was supported by the Scientific and Technological Research Council of Turkey (TUBITAK) through the 1515 Frontier Research and Development Laboratories Support Program under Project 5169902.

References

  • E. U. Soykan, L. Karaçay, F. Karakoç, and E. Tomur, “A survey and guideline on privacy enhancing technologies for collaborative machine learning,” IEEE Access, vol. 10, pp. 97495–97519, 2022. DOI: 10.1109/ACCESS.2022.3204037. [Online]. Available: https://doi.org/10.1109/ACCESS.2022.3204037.
  • B. McMahan, E. Moore, D. Ramage, S. Hampson, and B. A. y Arcas, “Communication efficient learning of deep networks from decentralized data,” in Artificial intelligence and statistics, PMLR, 2017, pp. 1273– 1282.
  • P. Kairouz, H. B. McMahan, B. Avent, et al., “Advances and open problems in federated learning,” Foundations and trends® in machine learning, vol. 14, no. 1–2, pp. 1–210, 2021.
  • D. Cao, S. Chang, Z. Lin, G. Liu, and D. Sun, “Understanding distributed poisoning attack in federated learning,” in 2019 IEEE 25th international conference on parallel and distributed systems (ICPADS), IEEE, 2019, pp. 233–239.
  • V. Mothukuri, R. M. Parizi, S. Pouriyeh, Y. Huang, A. Dehghantanha, and G. Srivastava, “A survey on security and privacy of federated learning,” Future Gener. Comput. Syst., vol. 115, pp. 619–640, 2021. DOI: 10.1016/J.FUTURE.2020.10.007. [Online]. Available: https://doi.org/10.1016/j.future.2020.10.007.
  • A. Blanco-Justicia, J. Domingo-Ferrer, S. Martínez, D. Sánchez, A. Flanagan, and K. E. Tan, “Achieving security and privacy in federated learning systems: Survey, research challenges and future directions,” Eng. Appl. Artif. Intell., vol. 106, p. 104 468, 2021. DOI: 10.1016/J.ENGAPPAI.2021.104468. [Online]. Available: https://doi.org/10.1016/j.engappai. 2021.104468.
  • N. B. Truong, K. Sun, S. Wang, F. Guitton, and Y. Guo, “Privacy preservation in federated learning: An insightful survey from the GDPR perspective,” Com- put. Secur., vol. 110, p. 102 402, 2021. DOI: 10.1016/J.COSE.2021.102402. [Online]. Available: https://doi.org/10.1016/j.cose.2021.102402.
  • N. Bouacida and P. Mohapatra, “Vulnerabilities in federated learning,” IEEE Access, vol. 9, pp. 63 229–63 249, 2021. DOI: 10.1109/ACCESS.2021.3075203. [Online]. Available: https://doi.org/10.1109/ACCESS.2021.3075203.
  • M. Mansouri, M. Önen, W. B. Jaballah, and M. Conti, “SoK: Secure aggregation based on cryptographic schemes for federated learning,” Proc. Priv. Enhancing Technol., vol. 2023, no. 1, pp. 140–157, 2023. DOI: 10.56553/POPETS- 2023- 0009. [Online]. Available: https://doi.org/10.56553/popets- 2023- 0009.
  • D. Enthoven and Z. Al-Ars, “An overview of federated deep learning privacy attacks and defensive strategies,” CoRR, vol. abs/2004.04676, 2020. arXiv: 2004.04676. [Online]. Available: https://arxiv.org/abs/2004.04676.
  • L. Lyu, H. Yu, X. Ma, et al., “Privacy and robustness in federated learning: Attacks and defenses,” CoRR, vol. abs/2012.06337, 2020. arXiv: 2012.06337. [Online]. Available: https://arxiv.org/abs/2012.06337.
  • J. Mao, C. Cao, L. Wang, J. Ye, and W. Zhong, “Research on the security technology of federated learning privacy preserving,” Journal of Physics: Conference Series, vol. 1757, no. 1, p. 012 192, Jan. 2021. DOI: 10.1088/1742-6596/1757/1/012192. [Online]. Available: https://dx.doi.org/10.1088/1742-6596/1757/1/012192.
  • M. Asad, S. Shaukat, D. Hu, et al., “Limitations and future aspects of communication costs in federated learning: A survey,” Sensors, vol. 23, no. 17, p. 7358, 2023. DOI: 10.3390/S23177358. [Online]. Available: https://doi.org/10.3390/s23177358.
  • A. Akhtarshenas, M. A. Vahedifar, N. Ayoobi, B. Ma- ham, T. Alizadeh, and S. Ebrahimi, “Federated learning: A cutting-edge survey of the latest advancements and applications,” CoRR, vol. abs/2310.05269, 2023. DOI: 10.48550/ARXIV.2310.05269. arXiv:2310.05269. [Online]. Available: https://doi.org/10.48550/arXiv.2310.05269.
  • D. Sirohi, N. Kumar, P. S. Rana, S. Tanwar, R. Iqbal, and M. Hijji, “Federated learning for 6G-enabled secure communication systems: A comprehensive survey,” Artif. Intell. Rev., vol. 56, no. 10, pp. 11297–11 389, 2023. DOI: 10.1007/S10462-023-10417-3. [Online]. Available: https://doi.org/10.1007/ s10462-023-10417-3.
  • M. Al-Quraan, L. S. Mohjazi, L. Bariah, et al., “Edge- native intelligence for 6G communications driven by federated learning: A survey of trends and challenges,” IEEE Trans. Emerg. Top. Comput. Intell., vol. 7, no. 3, pp. 957–979, 2023. DOI: 10.1109/TETCI.2023.3251404. [Online]. Available: https://doi.org/10.1109/TETCI.2023.3251404.
  • Y. Liu, X. Yuan, Z. Xiong, J. Kang, X. Wang, and D. Niyato, “Federated learning for 6G communications: Challenges, methods, and future directions,” CoRR, vol. abs/2006.02931, 2020. arXiv: 2006.02931. [Online]. Available: https ://arxiv.org/abs/2006.02931.
  • A. Rahman, K. Hasan, D. Kundu, et al., “On the ICN- IoT with federated learning integration of communication: Concepts, security-privacy issues, applications, and future perspectives,” Future Gener. Comput. Syst., vol. 138, pp. 61–88, 2023. DOI: 10.1016/J.FUTURE.2022.08.004. [Online]. Available: https://doi.org/10.1016/j.future.2022.08.004.
  • Y. Zuo, J. Guo, N. Gao, Y. Zhu, S. Jin, and X. Li, “A survey of blockchain and artificial intelligence for 6G wireless communications,” IEEE Commun. Surv. Tutorials, vol. 25, no. 4, pp. 2494–2528, 2023. DOI: 10.1109/COMST.2023.3315374. [Online]. Available: https://doi.org/10.1109/COMST.2023.3315374.
  • S. Abimannan, E.-S. M. El-Alfy, S. Hussain, et al., “Towards federated learning and multi-access edge computing for air quality monitoring: Literature review and assessment,” Sustainability, vol. 15, no. 18, 2023, ISSN: 2071-1050. DOI: 10.3390/su151813951. [Online]. Available: https://www.mdpi.com/2071- 1050/15/18/13951.
  • N. A. Khalek, D. H. Tashman, and W. Hamouda, “Advances in machine learning-driven cognitive radio for wireless networks: A survey,” IEEE Communications Surveys Tutorials, pp. 1–1, 2023. DOI: 10.1109/COMST.2023.3345796.
  • M. B. Driss, E. Sabir, H. Elbiaze, and W. Saad, “Federated learning for 6G: Paradigms, taxonomy, recent advances and insights,” CoRR, vol. abs/2312.04688, 2023. DOI:10.48550 / ARXIV . 2312 . 04688. arXiv: 2312.04688. [Online]. Available: https://doi.org/10.48550/arXiv.2312.04688.
  • M. A. Ferrag, O. Friha, B. Kantarci, et al., “Edge learning for 6G-enabled internet of things: A comprehensive survey of vulnerabilities, datasets, and defenses,” IEEE Commun. Surv. Tutorials, vol. 25, no. 4, pp. 2654–2713, 2023. DOI: 10.1109/COMST.2023.3317242. [Online]. Available: https://doi.org/10.1109/COMST.2023.3317242.
  • I. Bartsiokas, P. Gkonis, A. Papazafeiropoulos, D. Kaklamani, and I. Venieris, “Federated learning for 6G hetnets’ physical layer optimization: Perspectives, trends, and challenges federated learning for 6G het- nets’ physical layer optimization,” in Jul. 2024, p. 1– 28, ISBN: 9781668473665. DOI: 10 . 4018 / 978 - 1 -6684-7366-5.ch070.
  • J. M. P. Ullauri, X. Zhang, A. Bravalheri, Y. Wu, R. Ne- jabati, and D. Simeonidou, “Federated analytics for 6G networks: Applications, challenges, and opportunities,” CoRR, vol. abs/2401.03878, 2024. DOI: 10 . 48550/ARXIV.2401.03878. arXiv: 2401.03878. [Online]. Available: https://doi.org/10.48550/arXiv. 2401.03878.
  • S. K. Das, R. Mudi, M. S. Rahman, and A. O. Fapo- juwo, “Distributed learning for 6G–IoT networks: A comprehensive survey,” Authorea Preprints, 2023.
  • L. S. Mohjazi, B. Selim, M. Tatipamula, and M. A. Imran, “The journey towards 6G: A digital and societal revolution in the making,” CoRR, vol. abs/2306.00832, 2023. DOI: 10.48550 / ARXIV .2306.00832. arXiv: 2306.00832. [Online]. Available:https://doi.org/10.48550/arXiv.2306.00832.
  • C. Anitha, B. Balakiruthiga, S. Angayarkanni, P. P. Selvi, and L. S. Kumar, “Recent developments, application cases, and lingering issues on the path to a 6G IoT,” in 2023 International Conference on Research Methodologies in Knowledge Management, Artificial Intelligence and Telecommunication Engineering (RMKMATE), IEEE, 2023, pp. 1-10.
  • S. Polymeni, S. Plastras, D. N. Skoutas, G. Kormentzas, and C. Skianis, “The impact of 6G-IoT technologies on the development of agriculture 5.0: A review,” Electronics, vol. 12, no. 12, 2023, ISSN: 2079- 9292. DOI: 10.3390/electronics12122651. [Online]. Available: https://www.mdpi.com/2079- 9292/12/ 12/2651.
  • Y. Liu, J. Peng, J. Kang, A. M. Iliyasu, D. Niyato, and A. A. A. El-Latif, “A secure federated learning framework for 5G networks,” CoRR, vol. abs/2005.05752, 2020. arXiv: 2005.05752. [Online]. Available: https://arxiv.org/abs/2005.05752.
  • C. Zhou and N. Ansari, “Securing federated learning enabled NWDAF architecture with partial homomorphic encryption,” IEEE Netw. Lett., vol. 5, no. 4, pp. 299–303, 2023. DOI: 10 . 1109 / LNET . 2023 .3294497. [Online]. Available: https://doi.org/10. 1109/LNET.2023.3294497.
  • H. P. Phyu, R. Stanica, and D. Naboulsi, “Multi-slice privacy-aware traffic forecasting at RAN level: A scalable federated-learning approach,” IEEE Trans. Netw. Serv. Manag., vol. 20, no. 4, pp. 5038–5052, 2023. DOI:10.1109/ TNSM.2023.3267725. [Online]. Available: https ://doi.org/10.1109/TNSM.2023.3267725.
  • T. Hewa, P. Porambage, M. Liyanage, and M. Ylianttila, “Towards attack resistant federated learning with blockchain in 5G and beyond networks,” in 2023 Joint European Conference on Networks and Communications & 6G Summit, EuCNC/6G Summit 2023, Gothenburg, Sweden, June 6-9, 2023, Jun. 2023.
  • S. A. Khowaja, P. Khuwaja, K. Dev, and A. Antonopoulos, “SPIN: Simulated poisoning and inversion network for federated learning-based 6G vehicular networks,” in IEEE International Conference on Communications, ICC 2023, Rome, Italy, May 28- June 1, 2023, IEEE, 2023, pp. 6205–6210. DOI: 10.1109/ICC45041.2023.10279339. [Online]. Available: https://doi.org/10.1109/ICC45041.2023. 10279339.
  • S. P. Sanon, R. Reddy, C. Lipps, and H. D. Schotten, “Secure federated learning: An evaluation of homomorphic encrypted network traffic prediction,” in 20th IEEE Consumer Communications & Networking Conference, CCNC 2023, Las Vegas, NV, USA, January 8-11, 2023, IEEE, 2023, pp. 1–6. DOI: 10 . 1109 / CCNC51644 . 2023 . 10060116. [Online]. Available: https://doi.org/10.1109/CCNC51644.2023. 10060116.
  • M. Wasilewska, H. Bogucka, and H. V. Poor, “Secure federated learning for cognitive radio sensing,” CoRR, vol. abs/2304.06519, 2023. DOI: 10 .48550 / ARXIV .2304.06519. arXiv: 2304.06519. [Online]. Available: https://doi.org/10.48550/arXiv.2304.06519.
  • X. Lan, J. Taghia, F. Moradi, et al., “Federated learning for performance prediction in multi-operator environments,” ITU Journal on Future and Evolving Technologies, vol. 4, pp. 166–177, Mar. 2023. DOI: 10.52953/PFYZ9165.
  • T. Moulahi, R. Jabbar, A. Alabdulatif, et al., “Privacy- preserving federated learning cyber-threat detection for intelligent transport systems with blockchain- based security,” Expert Syst. J. Knowl. Eng., vol. 40, no. 5, 2023. DOI: 10.1111/EXSY.13103. [Online]. Available: https://doi.org/10.1111/exsy.13103.
  • A. A. Korba, A. Boualouache, B. Brik, R. Rahal, Y. Ghamri-Doudane, and S. M. Senouci, “Federated learning for zero-day attack detection in 5G and be- yond V2X networks,” in IEEE International Conference on Communications, ICC 2023, Rome, Italy, May 28 - June 1, 2023, IEEE, 2023, pp. 1137–1142. DOI: 10.1109/ICC45041.2023.10279368. [Online]. Available: https://doi.org/10.1109/ICC45041.2023.10279368.
  • A. Z. Rubina Akter and D.-S. Kim, “UAV-based B5G networks: Blockchain and federated learning technology,” 2023.
  • D. Sharma, A. Kumar, and R. B. Battula, “Fedbeam: Federated learning based privacy preserved localization for mass-beamforming in 5GB,” in International Conference on Information Networking, ICOIN 2023, Bangkok, Thailand, January 11-14, 2023, IEEE, 2023, pp. 616–621. DOI: 10 . 1109 / ICOIN56518 .2023 . 10048980. [Online]. Available: https://doi.org/10.1109/ICOIN56518.2023.10048980.
  • P. Rajabzadeh and A. Outtagarts, “Federated learning for distributed NWDAF architecture,” in 26th Conference on Innovation in Clouds, Internet and Networks, ICIN 2023, Paris, France, March 6-9, 2023, pp. 24–26. DOI: 10.1109/ICIN56760.2023.10073493. [Online]. Available: https://doi.org/10.1109/ICIN56760.2023.10073493.
  • A. Li, X. Chang, J. Ma, S. Sun, and Y. Yu, “VTFL: A blockchain based vehicular trustworthy federated learning framework,” in 2023 IEEE 6th Information Technology,Networking,Electronic and Automation Control Conference (ITNEC), vol. 6, 2023, pp. 1002–1006. DOI:10.1109/ITNEC56291.2023.10082698.
  • S. B. Saad, B. Brik, and A. Ksentini, “Toward securing federated learning against poisoning attacks in zero touch B5G networks,” IEEE Trans. Netw. Serv. Manag., vol. 20, no. 2, pp. 1612–1624, 2023. DOI: 10.1109/TNSM.2023.3278838. [Online]. Available: https://doi.org/10.1109/TNSM.2023.3278838.
  • J. Zhang, J. Zhang, D. W. K. Ng, and B. Ai, “Federated learning-based cell-free massive MIMO system for privacy-preserving,” IEEE Trans. Wirel. Commun., vol. 22, no. 7, pp. 4449–4460, 2023. DOI: 10.1109/TWC.2022.3225812. [Online]. Available: https://doi.org/10.1109/TWC.2022.3225812.
  • D. Ayepah-Mensah, G. Sun, G. O. Boateng, S. Anokye, and G. Liu, “Blockchain-enabled federated learning-based resource allocation and trading for network slicing in 5G,” IEEE/ACM Trans. Netw., vol. 32, no. 1, pp. 654–669, 2024. DOI: 10.1109/TNET.2023.3297390. [Online]. Available: https :// doi.org/10.1109/TNET.2023.3297390.
  • S. P. Sanon, C. Lipps, and H. D. Schotten, “Fully homomorphic encryption: Precision loss in wireless mobile communication,” in 2023 Joint European Conference on Networks and Communications & 6G Summit, EuCNC/6G Summit 2023, Gothenburg, Sweden, June 6-9, 2023, IEEE, 2023, pp. 466–471. DOI: 10.1109/EUCNC/6GSUMMIT58263.2023.10188286. [Online]. Available: https://doi.org/10.1109/EuCNC/6GSummit58263.2023.10188286.
  • W. Jiang, H. Han, Y. Zhang, and J. Mu, “Federated split learning for sequential data in satellite-terrestrial integrated networks,” Inf. Fusion, vol. 103, p. 102 141, 2024. DOI: 10.1016/J.INFFUS.2023.102141. [Online]. Available: https://doi.org/10.1016/j.inffus.2023.102141.
  • F. Wilhelmi, L. Giupponi, and P. Dini, “Blockchain- enabled Server-less Federated Learning,” CoRR, vol. abs/2112.07938, 2021. arXiv: 2112.07938. [Online]. Available: https://arxiv.org/abs/2112.07938.
  • I. A. Bartsiokas, P. K. Gkonis, D. I. Kaklamani, and I. S. Venieris, “A federated learning-based resource allocation scheme for relaying-assisted communications in multicellular next generation network topologies,” Electronics, vol. 13, no. 2, 2024, ISSN: 2079- 9292. DOI: 10.3390/electronics13020390. [Online]. Available: https://www.mdpi.com/2079- 9292/13/ 2/390.
  • D. Rahbari, M. M. Alam, Y. L. Moullec, and M. Jenihhin, “Applying RIS-based communication for collaborative computing in a swarm of drones,” IEEE Access, vol. 11, pp. 70 093–70 109, 2023. DOI: 10 . 1109 /ACCESS . 2023 . 3293737. [Online]. Available: https ://doi.org/10.1109/ACCESS.2023.3293737.
  • D. Javeed, M. Saeed, I. Ahmad, M. Adil, P. Kumar, and N. Islam, “Quantum-empowered federated learning and 6G wireless networks for IoT security: Concept, challenges and future directions,” Future Generation Computer Systems, Jun. 2024. DOI: 10.1016/j.future.2024.06.023.
  • J. Taghia, F. Moradi, H. Larsson, et al., “Congruent learning for self-regulated federated learning in 6G,” IEEE Transactions on Machine Learning in Communications and Networking, vol. 2, pp. 129–149, 2024. DOI: 10.1109/TMLCN.2023.3347680.
  • M. Al-Quraan, A. Zoha, A. Centeno, et al., “Enhancing reliability in federated mmwave networks: A practical and scalable solution using radar-aided dynamic blockage recognition,” CoRR, vol. abs/2307.06834, 2023. DOI: 10 . 48550 / ARXIV . 2307 . 06834. arXiv:2307.06834. [Online]. Available: https://doi.org/ 10.48550/arXiv.2307.06834.
  • M. Al-Quraan, A. Centeno, A. Zoha, M. A. Imran, and L. S. Mohjazi, “Federated learning for reliable mmwave systems: Vision-aided dynamic blockages prediction,” in IEEE Wireless Communications and Networking Conference, WCNC 2023, Glasgow, UK, March 26-29, 2023, IEEE, 2023, pp. 1–6. DOI: 10.1109/WCNC55385.2023.10118675. [Online]. Available: https://doi.org/10.1109/WCNC55385.2023. 10118675.
  • F. Karakoç, L. Karaçay, P. Ç. D. Cnudde, U. Gülen, R. Fuladi, and E. U. Soykan, “A security-friendly privacy- preserving solution for federated learning,” Comput. Commun., vol. 207, pp. 27–35, 2023. DOI: 10.1016/j.comcom.2023.05.004. [Online]. Available: https://doi.org/10.1016/j.comcom.2023.05.004.
  • M. Rathee, C. Shen, S. Wagh, and R. A. Popa, “ELSA: Secure aggregation for federated learning with malicious actors,” IACR Cryptol. ePrint Arch., p. 1695, 2022. [Online]. Available: https://eprint. iacr.org/2022/1695.
  • J. Domingo-Ferrer, A. Blanco-Justicia, J. A. Manjón, and D. Sánchez, “Secure and privacy-preserving federated learning via co-utility,” IEEE Internet Things J., vol. 9, no. 5, pp. 3988–4000, 2022. DOI: 10 . 1109 /JIOT . 2021 . 3102155. [Online]. Available: https://doi.org/10.1109/JIOT.2021.3102155.
  • H. Lycklama, L. Burkhalter, A. Viand, N. Küchler, and A. Hithnawi, “Rofl: Robustness of secure federated learning,” in 44th IEEE Symposium on Security and Privacy, SP 2023, San Francisco, CA, USA, May 21- 25, 2023, IEEE, 2023, pp. 453–476. DOI: 10.1109/SP46215.2023.10179400. [Online]. Available: https://doi.org/10.1109/SP46215.2023.10179400.
  • H. Masuda, K. Kita, Y. Koizumi, J. Takemasa, and T. Hasegawa, “Byzantine-resilient secure federated learning on low-bandwidth networks,” IEEE Access, vol. 11, pp. 51 754–51 766, 2023. DOI: 10 . 1109 / ACCESS.2023.3277858.
  • A. R. Chowdhury, C. Guo, S. Jha, and L. van der Maaten, “EIFFeL: Ensuring integrity for federated learning,” in Proceedings of the 2022 ACM SIGSAC Conference on Computer and Communications Security, CCS 2022, Los Angeles, CA, USA, November 7-11, 2022, pp. 2535–2549. DOI: 10.1145/3548606.
  • T. Gehlhar, F. Marx, T. Schneider, A. Suresh, T. Wehrle, and H. Yalame, “SafeFL: MPC-friendly framework for private and robust federated learning,” in 2023 IEEE Security and Privacy Workshops (SPW), San Francisco, CA, USA, May 25, 2023, IEEE, 2023, pp. 69–76. DOI: 10 . 1109 / SPW59333 .2023.00012. [Online]. Available: https://doi.org/ 10.1109/SPW59333.2023.00012.
  • X. Gu, M. Li, and L. Xiong, “DP-BREM: Differentially- private and byzantine-robust federated learning with client momentum,” CoRR, vol. abs/2306.12608, 2023. DOI: 10 . 48550 / ARXIV . 2306 . 12608. arXiv:2306.12608. [Online]. Available: https://doi.org/ 10.48550/arXiv.2306.12608.
  • Y. Ma, J. Woods, S. Angel, A. Polychroniadou, and T. Rabin, “Flamingo: Multi-round single-server secure aggregation with applications to private federated learning,” in 44th IEEE Symposium on Security and Privacy, SP 2023, San Francisco, CA, USA, May 21-25, 2023, IEEE, 2023, pp. 477–496. DOI: 10 . 1109 / SP46215 . 2023 . 10179434. [Online]. Available: https://doi.org/10.1109/SP46215.2023. 10179434.
  • Z. Ghodsi, M. Javaheripi, N. Sheybani, X. Zhang, K. Huang, and F. Koushanfar, “zPROBE: Zero peek robustness checks for federated learning,” in IEEE/CVF International Conference on Computer Vision, ICCV 2023, Paris, France, October 1-6, 2023, IEEE, 2023, pp. 4837–4847. DOI: 10 . 1109 / ICCV51070 . 2023 .00448. [Online]. Available: https://doi.org / 10 . 1109/ICCV51070.2023.00448.
  • H. Corrigan-Gibbs and D. Boneh, “Prio: Private, robust, and scalable computation of aggregate statistics,” CoRR, vol. abs/1703.06255, 2017. arXiv: 1703. 06255. [Online]. Available: http://arxiv.org/abs/ 1703.06255.

Mobil İletişim Ağları Alanında Güvenli ve Mahremiyet Odaklı Federe Öğrenme Üzerine Bir Araştırma

Year 2024, Volume: 1 Issue: 1, 29 - 40, 30.09.2024

Şükrü Erdal , Ferhat Karakoç , Enver Özdemir

Abstract

Bu çalışmada, veri gizliliği ile iş birliğine dayalı makine öğrenimi arasındaki boşluğu dolduran güvenlik odaklı, gizlilik artırılmış federe öğrenme için en son çözümleri ele alıyoruz. Analizimiz, mevcut metodolojilerin kapsamlı bir karşılaştırmalı değerlendirmesini sunarak, mevcut yaklaşımların güçlü ve zayıf yönlerine ışık tutuyor. Yeni bakış açıları sunarak, güvenli federe öğrenmenin sınırlarını zorlamayı ve öğrenme verimliliğinden ödün vermeden veri korumasını artıran teknikleri keşfetmeyi amaçlıyoruz. Ayrıca, merkezi olmayan sistemlerde ölçeklenebilir, mahremiyet odaklı mekanizmaların önemini vurgulayan ortaya çıkan zorluklar ve fırsatlara dikkat çekiyoruz. Federe öğrenme, sağlık hizmetleri, finans ve Nesnelerin Interneti gibi çeşitli sektörlerde hız kazanmaya devam ederken, çalışmamız, yenilik ve gelişim için önemli alanları belirleyerek, gelecekteki araştırmalar için bir temel niteliği taşıyor. Bu ileriye dönük yaklaşım, federe öğrenmenin hem mevcut hem de gelecekteki güvenlik kaygılarını ele alarak, gizliliğe duyarlı uygulamalar için güvenilir ve sağlam bir çözüm olarak gelişmeye devam etmesini sağlıyor.

Keywords

Federe öğrenme mahremiyet güvenli birleştirme homomorfik şifreleme çok taraflı güvenli hesaplama

Project Number

5169902

References

  • E. U. Soykan, L. Karaçay, F. Karakoç, and E. Tomur, “A survey and guideline on privacy enhancing technologies for collaborative machine learning,” IEEE Access, vol. 10, pp. 97495–97519, 2022. DOI: 10.1109/ACCESS.2022.3204037. [Online]. Available: https://doi.org/10.1109/ACCESS.2022.3204037.
  • B. McMahan, E. Moore, D. Ramage, S. Hampson, and B. A. y Arcas, “Communication efficient learning of deep networks from decentralized data,” in Artificial intelligence and statistics, PMLR, 2017, pp. 1273– 1282.
  • P. Kairouz, H. B. McMahan, B. Avent, et al., “Advances and open problems in federated learning,” Foundations and trends® in machine learning, vol. 14, no. 1–2, pp. 1–210, 2021.
  • D. Cao, S. Chang, Z. Lin, G. Liu, and D. Sun, “Understanding distributed poisoning attack in federated learning,” in 2019 IEEE 25th international conference on parallel and distributed systems (ICPADS), IEEE, 2019, pp. 233–239.
  • V. Mothukuri, R. M. Parizi, S. Pouriyeh, Y. Huang, A. Dehghantanha, and G. Srivastava, “A survey on security and privacy of federated learning,” Future Gener. Comput. Syst., vol. 115, pp. 619–640, 2021. DOI: 10.1016/J.FUTURE.2020.10.007. [Online]. Available: https://doi.org/10.1016/j.future.2020.10.007.
  • A. Blanco-Justicia, J. Domingo-Ferrer, S. Martínez, D. Sánchez, A. Flanagan, and K. E. Tan, “Achieving security and privacy in federated learning systems: Survey, research challenges and future directions,” Eng. Appl. Artif. Intell., vol. 106, p. 104 468, 2021. DOI: 10.1016/J.ENGAPPAI.2021.104468. [Online]. Available: https://doi.org/10.1016/j.engappai. 2021.104468.
  • N. B. Truong, K. Sun, S. Wang, F. Guitton, and Y. Guo, “Privacy preservation in federated learning: An insightful survey from the GDPR perspective,” Com- put. Secur., vol. 110, p. 102 402, 2021. DOI: 10.1016/J.COSE.2021.102402. [Online]. Available: https://doi.org/10.1016/j.cose.2021.102402.
  • N. Bouacida and P. Mohapatra, “Vulnerabilities in federated learning,” IEEE Access, vol. 9, pp. 63 229–63 249, 2021. DOI: 10.1109/ACCESS.2021.3075203. [Online]. Available: https://doi.org/10.1109/ACCESS.2021.3075203.
  • M. Mansouri, M. Önen, W. B. Jaballah, and M. Conti, “SoK: Secure aggregation based on cryptographic schemes for federated learning,” Proc. Priv. Enhancing Technol., vol. 2023, no. 1, pp. 140–157, 2023. DOI: 10.56553/POPETS- 2023- 0009. [Online]. Available: https://doi.org/10.56553/popets- 2023- 0009.
  • D. Enthoven and Z. Al-Ars, “An overview of federated deep learning privacy attacks and defensive strategies,” CoRR, vol. abs/2004.04676, 2020. arXiv: 2004.04676. [Online]. Available: https://arxiv.org/abs/2004.04676.
  • L. Lyu, H. Yu, X. Ma, et al., “Privacy and robustness in federated learning: Attacks and defenses,” CoRR, vol. abs/2012.06337, 2020. arXiv: 2012.06337. [Online]. Available: https://arxiv.org/abs/2012.06337.
  • J. Mao, C. Cao, L. Wang, J. Ye, and W. Zhong, “Research on the security technology of federated learning privacy preserving,” Journal of Physics: Conference Series, vol. 1757, no. 1, p. 012 192, Jan. 2021. DOI: 10.1088/1742-6596/1757/1/012192. [Online]. Available: https://dx.doi.org/10.1088/1742-6596/1757/1/012192.
  • M. Asad, S. Shaukat, D. Hu, et al., “Limitations and future aspects of communication costs in federated learning: A survey,” Sensors, vol. 23, no. 17, p. 7358, 2023. DOI: 10.3390/S23177358. [Online]. Available: https://doi.org/10.3390/s23177358.
  • A. Akhtarshenas, M. A. Vahedifar, N. Ayoobi, B. Ma- ham, T. Alizadeh, and S. Ebrahimi, “Federated learning: A cutting-edge survey of the latest advancements and applications,” CoRR, vol. abs/2310.05269, 2023. DOI: 10.48550/ARXIV.2310.05269. arXiv:2310.05269. [Online]. Available: https://doi.org/10.48550/arXiv.2310.05269.
  • D. Sirohi, N. Kumar, P. S. Rana, S. Tanwar, R. Iqbal, and M. Hijji, “Federated learning for 6G-enabled secure communication systems: A comprehensive survey,” Artif. Intell. Rev., vol. 56, no. 10, pp. 11297–11 389, 2023. DOI: 10.1007/S10462-023-10417-3. [Online]. Available: https://doi.org/10.1007/ s10462-023-10417-3.
  • M. Al-Quraan, L. S. Mohjazi, L. Bariah, et al., “Edge- native intelligence for 6G communications driven by federated learning: A survey of trends and challenges,” IEEE Trans. Emerg. Top. Comput. Intell., vol. 7, no. 3, pp. 957–979, 2023. DOI: 10.1109/TETCI.2023.3251404. [Online]. Available: https://doi.org/10.1109/TETCI.2023.3251404.
  • Y. Liu, X. Yuan, Z. Xiong, J. Kang, X. Wang, and D. Niyato, “Federated learning for 6G communications: Challenges, methods, and future directions,” CoRR, vol. abs/2006.02931, 2020. arXiv: 2006.02931. [Online]. Available: https ://arxiv.org/abs/2006.02931.
  • A. Rahman, K. Hasan, D. Kundu, et al., “On the ICN- IoT with federated learning integration of communication: Concepts, security-privacy issues, applications, and future perspectives,” Future Gener. Comput. Syst., vol. 138, pp. 61–88, 2023. DOI: 10.1016/J.FUTURE.2022.08.004. [Online]. Available: https://doi.org/10.1016/j.future.2022.08.004.
  • Y. Zuo, J. Guo, N. Gao, Y. Zhu, S. Jin, and X. Li, “A survey of blockchain and artificial intelligence for 6G wireless communications,” IEEE Commun. Surv. Tutorials, vol. 25, no. 4, pp. 2494–2528, 2023. DOI: 10.1109/COMST.2023.3315374. [Online]. Available: https://doi.org/10.1109/COMST.2023.3315374.
  • S. Abimannan, E.-S. M. El-Alfy, S. Hussain, et al., “Towards federated learning and multi-access edge computing for air quality monitoring: Literature review and assessment,” Sustainability, vol. 15, no. 18, 2023, ISSN: 2071-1050. DOI: 10.3390/su151813951. [Online]. Available: https://www.mdpi.com/2071- 1050/15/18/13951.
  • N. A. Khalek, D. H. Tashman, and W. Hamouda, “Advances in machine learning-driven cognitive radio for wireless networks: A survey,” IEEE Communications Surveys Tutorials, pp. 1–1, 2023. DOI: 10.1109/COMST.2023.3345796.
  • M. B. Driss, E. Sabir, H. Elbiaze, and W. Saad, “Federated learning for 6G: Paradigms, taxonomy, recent advances and insights,” CoRR, vol. abs/2312.04688, 2023. DOI:10.48550 / ARXIV . 2312 . 04688. arXiv: 2312.04688. [Online]. Available: https://doi.org/10.48550/arXiv.2312.04688.
  • M. A. Ferrag, O. Friha, B. Kantarci, et al., “Edge learning for 6G-enabled internet of things: A comprehensive survey of vulnerabilities, datasets, and defenses,” IEEE Commun. Surv. Tutorials, vol. 25, no. 4, pp. 2654–2713, 2023. DOI: 10.1109/COMST.2023.3317242. [Online]. Available: https://doi.org/10.1109/COMST.2023.3317242.
  • I. Bartsiokas, P. Gkonis, A. Papazafeiropoulos, D. Kaklamani, and I. Venieris, “Federated learning for 6G hetnets’ physical layer optimization: Perspectives, trends, and challenges federated learning for 6G het- nets’ physical layer optimization,” in Jul. 2024, p. 1– 28, ISBN: 9781668473665. DOI: 10 . 4018 / 978 - 1 -6684-7366-5.ch070.
  • J. M. P. Ullauri, X. Zhang, A. Bravalheri, Y. Wu, R. Ne- jabati, and D. Simeonidou, “Federated analytics for 6G networks: Applications, challenges, and opportunities,” CoRR, vol. abs/2401.03878, 2024. DOI: 10 . 48550/ARXIV.2401.03878. arXiv: 2401.03878. [Online]. Available: https://doi.org/10.48550/arXiv. 2401.03878.
  • S. K. Das, R. Mudi, M. S. Rahman, and A. O. Fapo- juwo, “Distributed learning for 6G–IoT networks: A comprehensive survey,” Authorea Preprints, 2023.
  • L. S. Mohjazi, B. Selim, M. Tatipamula, and M. A. Imran, “The journey towards 6G: A digital and societal revolution in the making,” CoRR, vol. abs/2306.00832, 2023. DOI: 10.48550 / ARXIV .2306.00832. arXiv: 2306.00832. [Online]. Available:https://doi.org/10.48550/arXiv.2306.00832.
  • C. Anitha, B. Balakiruthiga, S. Angayarkanni, P. P. Selvi, and L. S. Kumar, “Recent developments, application cases, and lingering issues on the path to a 6G IoT,” in 2023 International Conference on Research Methodologies in Knowledge Management, Artificial Intelligence and Telecommunication Engineering (RMKMATE), IEEE, 2023, pp. 1-10.
  • S. Polymeni, S. Plastras, D. N. Skoutas, G. Kormentzas, and C. Skianis, “The impact of 6G-IoT technologies on the development of agriculture 5.0: A review,” Electronics, vol. 12, no. 12, 2023, ISSN: 2079- 9292. DOI: 10.3390/electronics12122651. [Online]. Available: https://www.mdpi.com/2079- 9292/12/ 12/2651.
  • Y. Liu, J. Peng, J. Kang, A. M. Iliyasu, D. Niyato, and A. A. A. El-Latif, “A secure federated learning framework for 5G networks,” CoRR, vol. abs/2005.05752, 2020. arXiv: 2005.05752. [Online]. Available: https://arxiv.org/abs/2005.05752.
  • C. Zhou and N. Ansari, “Securing federated learning enabled NWDAF architecture with partial homomorphic encryption,” IEEE Netw. Lett., vol. 5, no. 4, pp. 299–303, 2023. DOI: 10 . 1109 / LNET . 2023 .3294497. [Online]. Available: https://doi.org/10. 1109/LNET.2023.3294497.
  • H. P. Phyu, R. Stanica, and D. Naboulsi, “Multi-slice privacy-aware traffic forecasting at RAN level: A scalable federated-learning approach,” IEEE Trans. Netw. Serv. Manag., vol. 20, no. 4, pp. 5038–5052, 2023. DOI:10.1109/ TNSM.2023.3267725. [Online]. Available: https ://doi.org/10.1109/TNSM.2023.3267725.
  • T. Hewa, P. Porambage, M. Liyanage, and M. Ylianttila, “Towards attack resistant federated learning with blockchain in 5G and beyond networks,” in 2023 Joint European Conference on Networks and Communications & 6G Summit, EuCNC/6G Summit 2023, Gothenburg, Sweden, June 6-9, 2023, Jun. 2023.
  • S. A. Khowaja, P. Khuwaja, K. Dev, and A. Antonopoulos, “SPIN: Simulated poisoning and inversion network for federated learning-based 6G vehicular networks,” in IEEE International Conference on Communications, ICC 2023, Rome, Italy, May 28- June 1, 2023, IEEE, 2023, pp. 6205–6210. DOI: 10.1109/ICC45041.2023.10279339. [Online]. Available: https://doi.org/10.1109/ICC45041.2023. 10279339.
  • S. P. Sanon, R. Reddy, C. Lipps, and H. D. Schotten, “Secure federated learning: An evaluation of homomorphic encrypted network traffic prediction,” in 20th IEEE Consumer Communications & Networking Conference, CCNC 2023, Las Vegas, NV, USA, January 8-11, 2023, IEEE, 2023, pp. 1–6. DOI: 10 . 1109 / CCNC51644 . 2023 . 10060116. [Online]. Available: https://doi.org/10.1109/CCNC51644.2023. 10060116.
  • M. Wasilewska, H. Bogucka, and H. V. Poor, “Secure federated learning for cognitive radio sensing,” CoRR, vol. abs/2304.06519, 2023. DOI: 10 .48550 / ARXIV .2304.06519. arXiv: 2304.06519. [Online]. Available: https://doi.org/10.48550/arXiv.2304.06519.
  • X. Lan, J. Taghia, F. Moradi, et al., “Federated learning for performance prediction in multi-operator environments,” ITU Journal on Future and Evolving Technologies, vol. 4, pp. 166–177, Mar. 2023. DOI: 10.52953/PFYZ9165.
  • T. Moulahi, R. Jabbar, A. Alabdulatif, et al., “Privacy- preserving federated learning cyber-threat detection for intelligent transport systems with blockchain- based security,” Expert Syst. J. Knowl. Eng., vol. 40, no. 5, 2023. DOI: 10.1111/EXSY.13103. [Online]. Available: https://doi.org/10.1111/exsy.13103.
  • A. A. Korba, A. Boualouache, B. Brik, R. Rahal, Y. Ghamri-Doudane, and S. M. Senouci, “Federated learning for zero-day attack detection in 5G and be- yond V2X networks,” in IEEE International Conference on Communications, ICC 2023, Rome, Italy, May 28 - June 1, 2023, IEEE, 2023, pp. 1137–1142. DOI: 10.1109/ICC45041.2023.10279368. [Online]. Available: https://doi.org/10.1109/ICC45041.2023.10279368.
  • A. Z. Rubina Akter and D.-S. Kim, “UAV-based B5G networks: Blockchain and federated learning technology,” 2023.
  • D. Sharma, A. Kumar, and R. B. Battula, “Fedbeam: Federated learning based privacy preserved localization for mass-beamforming in 5GB,” in International Conference on Information Networking, ICOIN 2023, Bangkok, Thailand, January 11-14, 2023, IEEE, 2023, pp. 616–621. DOI: 10 . 1109 / ICOIN56518 .2023 . 10048980. [Online]. Available: https://doi.org/10.1109/ICOIN56518.2023.10048980.
  • P. Rajabzadeh and A. Outtagarts, “Federated learning for distributed NWDAF architecture,” in 26th Conference on Innovation in Clouds, Internet and Networks, ICIN 2023, Paris, France, March 6-9, 2023, pp. 24–26. DOI: 10.1109/ICIN56760.2023.10073493. [Online]. Available: https://doi.org/10.1109/ICIN56760.2023.10073493.
  • A. Li, X. Chang, J. Ma, S. Sun, and Y. Yu, “VTFL: A blockchain based vehicular trustworthy federated learning framework,” in 2023 IEEE 6th Information Technology,Networking,Electronic and Automation Control Conference (ITNEC), vol. 6, 2023, pp. 1002–1006. DOI:10.1109/ITNEC56291.2023.10082698.
  • S. B. Saad, B. Brik, and A. Ksentini, “Toward securing federated learning against poisoning attacks in zero touch B5G networks,” IEEE Trans. Netw. Serv. Manag., vol. 20, no. 2, pp. 1612–1624, 2023. DOI: 10.1109/TNSM.2023.3278838. [Online]. Available: https://doi.org/10.1109/TNSM.2023.3278838.
  • J. Zhang, J. Zhang, D. W. K. Ng, and B. Ai, “Federated learning-based cell-free massive MIMO system for privacy-preserving,” IEEE Trans. Wirel. Commun., vol. 22, no. 7, pp. 4449–4460, 2023. DOI: 10.1109/TWC.2022.3225812. [Online]. Available: https://doi.org/10.1109/TWC.2022.3225812.
  • D. Ayepah-Mensah, G. Sun, G. O. Boateng, S. Anokye, and G. Liu, “Blockchain-enabled federated learning-based resource allocation and trading for network slicing in 5G,” IEEE/ACM Trans. Netw., vol. 32, no. 1, pp. 654–669, 2024. DOI: 10.1109/TNET.2023.3297390. [Online]. Available: https :// doi.org/10.1109/TNET.2023.3297390.
  • S. P. Sanon, C. Lipps, and H. D. Schotten, “Fully homomorphic encryption: Precision loss in wireless mobile communication,” in 2023 Joint European Conference on Networks and Communications & 6G Summit, EuCNC/6G Summit 2023, Gothenburg, Sweden, June 6-9, 2023, IEEE, 2023, pp. 466–471. DOI: 10.1109/EUCNC/6GSUMMIT58263.2023.10188286. [Online]. Available: https://doi.org/10.1109/EuCNC/6GSummit58263.2023.10188286.
  • W. Jiang, H. Han, Y. Zhang, and J. Mu, “Federated split learning for sequential data in satellite-terrestrial integrated networks,” Inf. Fusion, vol. 103, p. 102 141, 2024. DOI: 10.1016/J.INFFUS.2023.102141. [Online]. Available: https://doi.org/10.1016/j.inffus.2023.102141.
  • F. Wilhelmi, L. Giupponi, and P. Dini, “Blockchain- enabled Server-less Federated Learning,” CoRR, vol. abs/2112.07938, 2021. arXiv: 2112.07938. [Online]. Available: https://arxiv.org/abs/2112.07938.
  • I. A. Bartsiokas, P. K. Gkonis, D. I. Kaklamani, and I. S. Venieris, “A federated learning-based resource allocation scheme for relaying-assisted communications in multicellular next generation network topologies,” Electronics, vol. 13, no. 2, 2024, ISSN: 2079- 9292. DOI: 10.3390/electronics13020390. [Online]. Available: https://www.mdpi.com/2079- 9292/13/ 2/390.
  • D. Rahbari, M. M. Alam, Y. L. Moullec, and M. Jenihhin, “Applying RIS-based communication for collaborative computing in a swarm of drones,” IEEE Access, vol. 11, pp. 70 093–70 109, 2023. DOI: 10 . 1109 /ACCESS . 2023 . 3293737. [Online]. Available: https ://doi.org/10.1109/ACCESS.2023.3293737.
  • D. Javeed, M. Saeed, I. Ahmad, M. Adil, P. Kumar, and N. Islam, “Quantum-empowered federated learning and 6G wireless networks for IoT security: Concept, challenges and future directions,” Future Generation Computer Systems, Jun. 2024. DOI: 10.1016/j.future.2024.06.023.
  • J. Taghia, F. Moradi, H. Larsson, et al., “Congruent learning for self-regulated federated learning in 6G,” IEEE Transactions on Machine Learning in Communications and Networking, vol. 2, pp. 129–149, 2024. DOI: 10.1109/TMLCN.2023.3347680.
  • M. Al-Quraan, A. Zoha, A. Centeno, et al., “Enhancing reliability in federated mmwave networks: A practical and scalable solution using radar-aided dynamic blockage recognition,” CoRR, vol. abs/2307.06834, 2023. DOI: 10 . 48550 / ARXIV . 2307 . 06834. arXiv:2307.06834. [Online]. Available: https://doi.org/ 10.48550/arXiv.2307.06834.
  • M. Al-Quraan, A. Centeno, A. Zoha, M. A. Imran, and L. S. Mohjazi, “Federated learning for reliable mmwave systems: Vision-aided dynamic blockages prediction,” in IEEE Wireless Communications and Networking Conference, WCNC 2023, Glasgow, UK, March 26-29, 2023, IEEE, 2023, pp. 1–6. DOI: 10.1109/WCNC55385.2023.10118675. [Online]. Available: https://doi.org/10.1109/WCNC55385.2023. 10118675.
  • F. Karakoç, L. Karaçay, P. Ç. D. Cnudde, U. Gülen, R. Fuladi, and E. U. Soykan, “A security-friendly privacy- preserving solution for federated learning,” Comput. Commun., vol. 207, pp. 27–35, 2023. DOI: 10.1016/j.comcom.2023.05.004. [Online]. Available: https://doi.org/10.1016/j.comcom.2023.05.004.
  • M. Rathee, C. Shen, S. Wagh, and R. A. Popa, “ELSA: Secure aggregation for federated learning with malicious actors,” IACR Cryptol. ePrint Arch., p. 1695, 2022. [Online]. Available: https://eprint. iacr.org/2022/1695.
  • J. Domingo-Ferrer, A. Blanco-Justicia, J. A. Manjón, and D. Sánchez, “Secure and privacy-preserving federated learning via co-utility,” IEEE Internet Things J., vol. 9, no. 5, pp. 3988–4000, 2022. DOI: 10 . 1109 /JIOT . 2021 . 3102155. [Online]. Available: https://doi.org/10.1109/JIOT.2021.3102155.
  • H. Lycklama, L. Burkhalter, A. Viand, N. Küchler, and A. Hithnawi, “Rofl: Robustness of secure federated learning,” in 44th IEEE Symposium on Security and Privacy, SP 2023, San Francisco, CA, USA, May 21- 25, 2023, IEEE, 2023, pp. 453–476. DOI: 10.1109/SP46215.2023.10179400. [Online]. Available: https://doi.org/10.1109/SP46215.2023.10179400.
  • H. Masuda, K. Kita, Y. Koizumi, J. Takemasa, and T. Hasegawa, “Byzantine-resilient secure federated learning on low-bandwidth networks,” IEEE Access, vol. 11, pp. 51 754–51 766, 2023. DOI: 10 . 1109 / ACCESS.2023.3277858.
  • A. R. Chowdhury, C. Guo, S. Jha, and L. van der Maaten, “EIFFeL: Ensuring integrity for federated learning,” in Proceedings of the 2022 ACM SIGSAC Conference on Computer and Communications Security, CCS 2022, Los Angeles, CA, USA, November 7-11, 2022, pp. 2535–2549. DOI: 10.1145/3548606.
  • T. Gehlhar, F. Marx, T. Schneider, A. Suresh, T. Wehrle, and H. Yalame, “SafeFL: MPC-friendly framework for private and robust federated learning,” in 2023 IEEE Security and Privacy Workshops (SPW), San Francisco, CA, USA, May 25, 2023, IEEE, 2023, pp. 69–76. DOI: 10 . 1109 / SPW59333 .2023.00012. [Online]. Available: https://doi.org/ 10.1109/SPW59333.2023.00012.
  • X. Gu, M. Li, and L. Xiong, “DP-BREM: Differentially- private and byzantine-robust federated learning with client momentum,” CoRR, vol. abs/2306.12608, 2023. DOI: 10 . 48550 / ARXIV . 2306 . 12608. arXiv:2306.12608. [Online]. Available: https://doi.org/ 10.48550/arXiv.2306.12608.
  • Y. Ma, J. Woods, S. Angel, A. Polychroniadou, and T. Rabin, “Flamingo: Multi-round single-server secure aggregation with applications to private federated learning,” in 44th IEEE Symposium on Security and Privacy, SP 2023, San Francisco, CA, USA, May 21-25, 2023, IEEE, 2023, pp. 477–496. DOI: 10 . 1109 / SP46215 . 2023 . 10179434. [Online]. Available: https://doi.org/10.1109/SP46215.2023. 10179434.
  • Z. Ghodsi, M. Javaheripi, N. Sheybani, X. Zhang, K. Huang, and F. Koushanfar, “zPROBE: Zero peek robustness checks for federated learning,” in IEEE/CVF International Conference on Computer Vision, ICCV 2023, Paris, France, October 1-6, 2023, IEEE, 2023, pp. 4837–4847. DOI: 10 . 1109 / ICCV51070 . 2023 .00448. [Online]. Available: https://doi.org / 10 . 1109/ICCV51070.2023.00448.
  • H. Corrigan-Gibbs and D. Boneh, “Prio: Private, robust, and scalable computation of aggregate statistics,” CoRR, vol. abs/1703.06255, 2017. arXiv: 1703. 06255. [Online]. Available: http://arxiv.org/abs/ 1703.06255.
There are 66 citations in total.

Details

Primary Language English
Subjects Data Security and Protection, Data and Information Privacy
Journal Section Reviews
Authors

Şükrü Erdal 0009-0003-4082-0564

Ferhat Karakoç 0000-0001-6139-6668

Enver Özdemir 0000-0001-8130-8178

Project Number 5169902
Publication Date September 30, 2024
Submission Date May 12, 2024
Acceptance Date June 2, 2024
Published in Issue Year 2024 Volume: 1 Issue: 1

Cite

IEEE Ş. Erdal, F. Karakoç, and E. Özdemir, “A Survey On Security and Privacy Aspects and Solutions for Federated Learning in Mobile Communication Networks”, ITU JWCC, vol. 1, no. 1, pp. 29–40, 2024.
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