21 citations · 49 across the 11 of their papers we have counts for
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cs.LG2022★ 6 cited
Refined Convergence and Topology Learning for Decentralized SGD with Heterogeneous Data
Batiste Le Bars, Aurélien Bellet, Marc Tommasi +2
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…
cs.LG2021
D-Cliques: Compensating for Data Heterogeneity with Topology in Decentralized Federated Learning
Aurélien Bellet, Anne-Marie Kermarrec, Erick Lavoie
The convergence speed of machine learning models trained with Federated Learning is significantly affected by heterogeneous data partitions, even more so in a fully decentralized s…