DeceFL: a principled fully decentralized federated learning framework
Ye Yuan, Jun Liu, Dou Jin, Zuogong Yue, Tao Yang, Ruijuan Chen, Maolin Wang, Lei Xu, Feng Hua, Yuqi Guo, Xiuchuan Tang, Xin He, Xinlei Yi, Dong Li, Wenwu Yu, Haitao Zhang, Tianyou Chai, Shaochun Sui, Han Ding
Abstract
Traditional machine learning relies on a centralized data pipeline for model training in various applications; however, data are inherently fragmented. Such a decentralized nature of databases presents the serious challenge for collaboration: sending all decentralized datasets to a central server raises serious privacy concerns. Although there has been a joint effort in tackling such a critical issue by proposing privacy-preserving machine learning frameworks, such as federated learning, most state-of-the-art frameworks are built still in a centralized way, in which a central client is needed for collecting and distributing model information (instead of data itself) from every other client, leading to high communication burden and high vulnerability when there exists a failure at or an attack on the central client. Here we propose a principled decentralized federated learning algorithm (DeceFL), which does not require a central client and relies only on local information transmission between clients and their neighbors, representing a fully decentralized learning framework. It has been further proven that every client reaches the global minimum with zero performance gap and achieves the same convergence rate $O(1/T)$ (where $T$ is the number of iterations in gradient descent) as centralized federated learning when the loss function is smooth and strongly convex. Finally, the proposed algorithm has been applied to a number of applications to illustrate its effectiveness for both convex and nonconvex loss functions, time-invariant and time-varying topologies, as well as IID and Non-IID of datasets, demonstrating its applicability to a wide range of real-world medical and industrial applications.