FedLite: A Scalable Approach for Federated Learning on Resource-constrained Clients. (arXiv:2201.11865v1 [cs.LG])

Web: http://arxiv.org/abs/2201.11865

In classical federated learning, the clients contribute to the overall
training by communicating local updates for the underlying model on their
private data to a coordinating server. However, updating and communicating the
entire model becomes prohibitively expensive when resource-constrained clients
collectively aim to train a large machine learning model. Split learning
provides a natural solution in such a setting, where only a small part of the
model is stored and trained on clients while the remaining large part of the …

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