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A recent emphasis of distributed learning research has been on federated learning (FL), in which model training is conducted by the data-collecting devices.
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2019
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2019
Cited alongside, same era.
T. Li, A. K. Sahu, A. Talwalkar, and V. Smith, “Federated learning: Challenges, methods, and future directions,” IEEE Signal Proc. Mag
2020
Cited alongside, same era.
S. Hosseinalipour, C. G. Brinton, V. Aggarwal, H. Dai, and M. Chiang, “From federated to fog learning: Distributed machine learning over heterogeneous wireless networks,” IEEE Commun. Mag
2020
Cited alongside, same era.
H. T. Nguyen, V. Sehwag, S. Hosseinalipour, C. G. Brinton, M. Chiang, and H. V. Poor, “Fast-convergent federated learning,” IEEE J. Sel. Areas Commun
2020
Cited alongside, same era.
X. Gu, K. Huang, J. Zhang, and L. Huang, “Fast federated learning in the presence of arbitrary device unavailability,” Advances Neur. Info. Process. Sys. (NeurIPS)
2021
Later among the works it cites.
F. P.-C. Lin, S. Hosseinalipour, S. S. Azam, C. G. Brinton, and N. Michelusi, “Semi-decentralized federated learning with cooperative D2D local model aggregations,” IEEE J. Sel. Areas Commun
2021
Later among the works it cites.
S. Hosseinalipour, S. S. Azam, C. G. Brinton, N. Michelusi, V. Aggarwal, D. J. Love, and H. Dai, “Multi-stage hybrid federated learning over large-scale D2D-enabled fog networks,” IEEE/ACM Trans. Network
2022
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