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A Snapshot of the Frontiers of Client Selection in Federated Learning. (arXiv:2210.04607v2 [cs.DC] UPDATED)
cs.CR updates on arXiv.org arxiv.org
Federated learning (FL) has been proposed as a privacy-preserving approach in
distributed machine learning. A federated learning architecture consists of a
central server and a number of clients that have access to private, potentially
sensitive data. Clients are able to keep their data in their local machines and
only share their locally trained model's parameters with a central server that
manages the collaborative learning process. FL has delivered promising results
in real-life scenarios, such as healthcare, energy, and finance. However, …
access architecture client clients data distributed federated learning local machine machine learning privacy private sensitive data server