Web: http://arxiv.org/abs/2201.11986

Jan. 31, 2022, 2:11 a.m. | Irene Tenison, Sai Aravind Sreeramadas, Vaikkunth Mugunthan, Edouard Oyallon, Eugene Belilovsky, Irina Rish

cs.LG updates on arXiv.org arxiv.org

Federated learning is an emerging paradigm that permits a large number of
clients with heterogeneous data to coordinate learning of a unified global
model without the need to share data amongst each other. Standard federated
learning algorithms involve averaging of model parameters or gradient updates
to approximate the global model at the server. However, in heterogeneous
settings averaging can result in information loss and lead to poor
generalization due to the bias induced by dominant clients. We hypothesize that
to …

arxiv federated learning gradient learning

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