Federated Learning

Definitions & Characteristics

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References

Note: The sources marked in purple are ideal for further reading, particularly for a non-technical audience. Tester
Coursera. Machine Learning Models: What They Are and How to Build Them. 2023. (visited on 08/06/2023).
A. Shteyn, K. Kollnig, and C. Inverarity. Federated learning: an introduction. Tech. rep. Open Data Institute, 2023.
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.
Talend, Inc. What are Data Silos? (visited on 04/26/2023).
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.
P. Kairouz, H. B. McMahan, B. Avent, A. Bellet, M. Bennis, A. N. Bhagoji, K. Bonawitz, Z. Charles, G. Cormode, R. Cummings, et al. “Advances and Open Problems in Federated Learning”. In: (2021). arXiv: 1912.04977 [cs.LG].
S. Tao. A brief introduction to Federated Learning — FL Series Part 1. 2021. (visited on 04/26/2023).

References

Note: The sources marked in purple are ideal for further reading, particularly for a non-technical audience. Tester
D. C. Nguyen, Q.-V. Pham, P. N. Pathirana, M. Ding, A. Seneviratne, Z. Lin, O. Dobre, and W.-J. Hwang. “Federated learning for smart healthcare: A survey”. In: ACM Computing Surveys (CSUR) 55.3 (2022), pp. 1–37. doi: 10.1145/3501296.
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.
Q. Yang, Y. Liu, T. Chen, and Y. Tong. “Federated machine learning: Concept and applications”. In: ACM Transactions on Intelligent Systems and Technology (TIST) 10.2 (2019), pp. 1–19. doi: 10.1145/3298981.
Google Developers. Descending into ML: Training and Loss. (visited on 04/26/2023).

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