Decentralized Federated Learning: Fundamentals, State-of-the-art, Frameworks, Trends, and Challenges. (arXiv:2211.08413v1 [cs.LG])

In the last decade, Federated Learning (FL) has gained relevance in training
collaborative models without sharing sensitive data. Since its birth,
Centralized FL (CFL) has been the most common approach in the literature, where
a unique entity creates global models. However, using a centralized approach
has the disadvantages of bottleneck at the server node, single point of
failure, and trust needs. Decentralized Federated Learning (DFL) arose to solve
these aspects by embracing the principles of data sharing minimization and
decentralized …

art challenges decentralized federated learning frameworks fundamentals state trends