Web: http://arxiv.org/abs/2205.02719

June 20, 2022, 1:11 a.m. | Yujia Wang, Lu Lin, Jinghui Chen

cs.LG updates on arXiv.org arxiv.org

Federated learning is a machine learning training paradigm that enables
clients to jointly train models without sharing their own localized data.
However, the implementation of federated learning in practice still faces
numerous challenges, such as the large communication overhead due to the
repetitive server-client synchronization and the lack of adaptivity by
SGD-based model updates. Despite that various methods have been proposed for
reducing the communication cost by gradient compression or quantization, and
the federated versions of adaptive optimizers such as …

arxiv communication federated learning learning lg

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