A Survey on Decentralized Federated Learning. (arXiv:2308.04604v1 [cs.LG])

In recent years, federated learning (FL) has become a very popular paradigm
for training distributed, large-scale, and privacy-preserving machine learning
(ML) systems. In contrast to standard ML, where data must be collected at the
exact location where training is performed, FL takes advantage of the
computational capabilities of millions of edge devices to collaboratively train
a shared, global model without disclosing their local private data.
Specifically, in a typical FL system, the central server acts only as an
orchestrator; it …

capabilities computational data decentralized devices distributed edge edge devices federated learning large location machine machine learning paradigm popular privacy scale standard survey systems training