Federated Learning for 6G Communications: Challenges, Methods, and Future Directions

K. Bonawitz, H. Eichner, W. Grieskamp, D. Huba, A. Ingerman, V. Ivanov, C. M. Kiddon, J. Konečný, S. Mazzocchi, B. McMahan, T. V. Overveldt, D. Petrou, D. Ramage, and J. Roselander, “Towards federated learning at scale: System design,” in SysML 2019 , 2019, to appear. [Online]. Available: https://arxiv.org/abs/1902.01046

Original

1902

Earlier work this paper cites.

A. F. Atiya and A. G. Parlos, “New results on recurrent network training: unifying the algorithms and accelerating convergence,” IEEE transactions on neural networks , vol. 11, no. 3, pp. 697–709, 2000

2000

Earlier work this paper cites.

V. Kulkarni, M. Kulkarni, and A. Pant, “Survey of personalization techniques for federated learning,” arXiv preprint arXiv:2003.08673 , 2020

Original

2006

Earlier work this paper cites.

J. Konečnỳ, H. B. McMahan, F. X. Yu, P. Richtárik, A. T. Suresh, and D. Bacon, “Federated learning: Strategies for improving communication efficiency,” arXiv preprint arXiv:1610.05492 , 2016

Original

2016

Earlier work this paper cites.

M. Abadi, A. Chu, I. Goodfellow, H. B. McMahan, I. Mironov, K. Talwar, and L. Zhang, “Deep learning with differential privacy,” in Proceedings of the 2016 ACM SIGSAC Conference on Computer and Communications Security , 2016, pp. 308–318

2016

Earlier work this paper cites.

R. C. Geyer, T. Klein, and M. Nabi, “Differentially private federated learning: A client level perspective,” arXiv preprint arXiv:1712.07557 , 2017

Original

2017

Earlier work this paper cites.

Z. Jiang, A. Balu, C. Hegde, and S. Sarkar, “Collaborative deep learning in fixed topology networks,” in Advances in Neural Information Processing Systems , 2017, pp. 5904–5914

2017

Earlier work this paper cites.

K. David and H. Berndt, “6g vision and requirements: Is there any need for beyond 5g?” IEEE Vehicular Technology Magazine , vol. 13, no. 3, pp. 72–80, 2018

2018

Earlier work this paper cites.

Y. Lin, S. Han, H. Mao, Y. Wang, and B. Dally, “Deep gradient compression: Reducing the communication bandwidth for distributed training,” in International Conference on Learning Representations , 2018. [Online]. Available: https://openreview.net/forum?id=SkhQHMW0W

2018

Earlier work this paper cites.

G. Yang, Q. Zhang, and Y.-C. Liang, “Cooperative ambient backscatter communications for green internet-of-things,” IEEE Internet of Things Journal , vol. 5, no. 2, pp. 1116–1130, 2018

2018

Earlier work this paper cites.

S. Gu, J. Jiao, Z. Huang, S. Wu, and Q. Zhang, “Arma-based adaptive coding transmission over millimeter-wave channel for integrated satellite-terrestrial networks,” IEEE Access , vol. 6, pp. 21 635–21 645, 2018

2018

Earlier work this paper cites.

E. Jeong, S. Oh, H. Kim, J. Park, M. Bennis, and S.-L. Kim, “Communication-efficient on-device machine learning: Federated distillation and augmentation under non-iid private data,” arXiv preprint arXiv:1811.11479 , 2018

Original

2018

Earlier work this paper cites.

K. B. Letaief, W. Chen, Y. Shi, J. Zhang, and Y.-J. A. Zhang, “The roadmap to 6g: Ai empowered wireless networks,” IEEE Communications Magazine , vol. 57, no. 8, pp. 84–90, 2019

2019

Earlier work this paper cites.

S. Niknam, H. S. Dhillon, and J. H. Reed, “Federated learning for wireless communications: Motivation, opportunities and challenges,” arXiv preprint arXiv:1908.06847 , 2019

Original

2019

Earlier work this paper cites.

J. Kang, Z. Xiong, D. Niyato, S. Xie, and J. Zhang, “Incentive mechanism for reliable federated learning: A joint optimization approach to combining reputation and contract theory,” IEEE Internet of Things Journal , vol. 6, no. 6, pp. 10 700–10 714, 2019

2019

Earlier work this paper cites.

T. Huang, W. Yang, J. Wu, J. Ma, X. Zhang, and D. Zhang, “A survey on green 6g network: Architecture and technologies,” IEEE Access , vol. 7, pp. 175 758–175 768, 2019

2019

Earlier work this paper cites.

R. Long, H. Guo, L. Zhang, and Y.-C. Liang, “Full-duplex backscatter communications in symbiotic radio systems,” IEEE Access , vol. 7, pp. 21 597–21 608, 2019

2019

Earlier work this paper cites.

Z. Zhang, Y. Xiao, Z. Ma, M. Xiao, Z. Ding, X. Lei, G. K. Karagiannidis, and P. Fan, “6g wireless networks: Vision, requirements, architecture, and key technologies,” IEEE Vehicular Technology Magazine , vol. 14, no. 3, pp. 28–41, 2019

2019

Cited alongside, same era.

M. Chen, Z. Yang, W. Saad, C. Yin, H. V. Poor, and S. Cui, “A joint learning and communications framework for federated learning over wireless networks,” arXiv preprint arXiv:1909.07972 , 2019

Original

2019

Cited alongside, same era.

A. Souri, A. Hussien, M. Hoseyninezhad, and M. Norouzi, “A systematic review of iot communication strategies for an efficient smart environment,” Transactions on Emerging Telecommunications Technologies , p. e3736, 2019

2019

Cited alongside, same era.

L. U. Khan, N. H. Tran, S. R. Pandey, W. Saad, Z. Han, M. N. Nguyen, and C. S. Hong, “Federated learning for edge networks: Resource optimization and incentive mechanism,” arXiv preprint arXiv:1911.05642 , 2019

Original

2019

M. Giordani, M. Polese, M. Mezzavilla, S. Rangan, and M. Zorzi, “Toward 6g networks: Use cases and technologies,” IEEE Communications Magazine , vol. 58, no. 3, pp. 55–61, 2020

2020

Closest in time.

S. Nayak and R. Patgiri, “6g communication technology: A vision on intelligent healthcare,” arXiv preprint arXiv:2005.07532 , 2020

Original

2020

Closest in time.

M. Giordani, M. Polese, M. Mezzavilla, S. Rangan, and M. Zorzi, “Toward 6g networks: Use cases and technologies,” IEEE Communications Magazine , vol. 58, no. 3, pp. 55–61, 2020

2020

Closest in time.

Y. Liu, J. J. Q. Yu, J. Kang, D. Niyato, and S. Zhang, “Privacy-preserving traffic flow prediction: A federated learning approach,” IEEE Internet of Things Journal , pp. 1–1, 2020

2020

Closest in time.

A. Portnoy and D. Hendler, “Towards realistic byzantine-robust federated learning,” arXiv preprint arXiv:2004.04986 , 2020