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Autor:
Bars, B.L. (Batiste Le), Bellet, A. (Aurelien), Tommasi, M. (Marc), Lavoie, E. (Erick), Kermarrec, A-M. (Anne-Marie)
One of the key challenges in decentralized and federated learning is to design algorithms that efficiently deal with highly heterogeneous data distributions across agents. In this paper, we revisit the analysis of the popular Decentralized Stochastic
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=od______4198::f0acb486ddd89d2087806600184ef655
http://hdl.handle.net/20.500.12210/80018
http://hdl.handle.net/20.500.12210/80018
