Federated Learning

Benefits & Challenges

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References

Note: The sources marked in purple are ideal for further reading, particularly for a non-technical audience. Tester
A. Shteyn, K. Kollnig, and C. Inverarity. Federated learning: an introduction. Tech. rep. Open Data Institute, 2023.
M. Aledhari, R. Razzak, R. M. Parizi, and F. Saeed. “Federated learning: A survey on enabling technologies, protocols, and applications”. In: IEEE Access 8 (2020), pp.140699– 140725. doi: 10.1109/ACCESS.2020.3013541
B. McMahan, E. Moore, D. Ramage, S. Hampson, and B. A. y Arcas. “Communication-efficient learning of deep networks from decentralized data”. In: Proceedings of the 20th International Conference on Artificial Intelligence and Statistics. Vol. 54. PMLR. 2017, pp. 1273–1282.
W. Y. B. Lim, N. C. Luong, D. T. Hoang, Y. Jiao, Y.-C. Liang, Q. Yang, D. Niyato, and C. Miao. “Federated learning in mobile edge networks: A comprehensive survey”. In: IEEE Communications Surveys & Tutorials 22.3 (2020), pp. 2031–2063. doi: 10.1109/COMST.2020.2986024.
M. Aledhari, R. Razzak, R. M. Parizi, and F. Saeed. “Federated learning: A survey on enabling technologies, protocols, and applications”. In: IEEE Access 8 (2020), pp. 140699–140725. doi: 10.1109/ACCESS.2020.3013541.
T. Li, A. K. Sahu, A. Talwalkar, and V. Smith. “Federated learning: Challenges, methods, and future directions”. In: IEEE signal processing magazine 37.3 (2020), pp. 50–60. doi: 10.1109/MSP.2020.2975749.

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