Federated Learning (FL) is a collaborative machine learning approach that enables decentralized data processing. Instead of collecting and storing data in a central server, FL trains machine learning models directly on devices or servers where the data resides, enhancing privacy and security.
security privacy blockchain mobile-devices multi-party-computation federated-learning data-heterogeneity decentralized-training communication-overhead client-reliability advanced-client-selection greedy-selection reputation-based-schemes
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Updated
Jul 18, 2024 - Python