Federated Learning

Applications & Examples

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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.
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.
M. Hughes. Personal info of 31 million people leaked by popular virtual keyboard Ai.type. 2017. (visited on 04/26/2023).
M. Blake, J. McWaters, and R. Galaski. “The next generation of data-sharing in financial services: Using privacy enhancing techniques to unlock new value”. In: World Economic Forum. 2019, pp. 1–36.
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.

References

Note: The sources marked in purple are ideal for further reading, particularly for a non-technical audience. Tester
Gartner. Gartner Says Digital Ethics is at the Peak of Inflated Expectations in the 2021 Gartner Hype Cycle for Privacy. 2021. (visited on 07/11/2023).

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