Decentralized Federated Learning: A Survey and Perspective

By Liangqi Yuan, Lichao Sun, Philip S. Yu, Ziran Wang. IEEE Internet of Things Journal, 2023

320 citations recorded by the source corpus.

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 article, a comprehensive survey and profound perspective are 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 (SOTA) technologies. Lastly, in addition to summarizing the current challenges in the DFL, some possible solutions and future research directions are also discussed.

Subfields: Artificial Intelligence, Electrical and Electronic Engineering, Sociology and Political Science

View Paper: https://www.semanticscholar.org/paper/f48d40617405e6fdd9b720d91a0bc557f9c51900