Decentralized Federated Learning: A Survey and Perspective
Liangqi Yuan Lichao Sun Philip S. Yu Ziran Wang
Federated learning (FL) has been gaining attention for its ability to share
knowledge while maintaining user data, protecting privacy, increasing learning
efficiency, and reducing communication overhead. Decentralized FL (DFL) is a
decentralized network architecture that eliminates the need for a central
server in contrast to centralized FL (CFL). DFL enables direct communication
between clients, resulting in significant savings in communication resources.
In this paper, a comprehensive survey and profound perspective is provided for
DFL. First, a review of the methodology, challenges, and variants of CFL is
conducted, laying the background of DFL. Then, a systematic and detailed
perspective on DFL is introduced, including iteration order, communication
protocols, network topologies, paradigm proposals, and temporal variability.
Next, based on the definition of DFL, several extended variants and
categorizations are proposed with state-of-the-art technologies. Lastly, in
addition to summarizing the current challenges in the DFL, some possible
solutions and future research directions are also discussed.