Boosting the Performance of Decentralized Federated Learning via Catalyst Acceleration

--Decentralized Federated Learning has emerged as an alternative to centralized architectures due to its faster training, privacy preservation, and reduced communication overhead. In decentralized communication, the server aggregation phase in Centralized Federated Learning shifts to the client side, which means that clients connect with each other in a peer-to-peer manner . However, compared to the centralized mode, data heterogeneity in Decentralized Federated Learning will cause larger variances between aggregated models, which leads to slow convergence in training and poor generalization performance in tests. T o address these issues, we introduce Catalyst Acceleration and propose an acceleration Decentralized Federated Learning algorithm called DFedCata. It consists of two main components: the Moreau envelope function, which primarily addresses parameter inconsistencies among clients caused by data heterogeneity, and Nesterov's extrapolation step, which accelerates the aggregation phase. Theoretically, We prove the optimization error bound and generalization error bound of the algorithm, providing a further understanding of the nature of the algorithm and the theoretical perspectives on the hyperparameter choice. Empirically, we demonstrate the advantages of the proposed algorithm in both convergence speed and generalization performance on CIF AR10/100 with various non-iid data distributions. Furthermore, we also experimentally verify the theoretical properties of DFedCata. EDERA TED Learning (FL) is a new distributed machine learning paradigm that prioritizes privacy protection [1]- [3]. It enables multiple clients to collaborate on training models without sharing their raw data. Nowadays, much of the research [4]-[9] focus on Centralized Federated Learning (CFL), but the central server in CFL brings various challenges on communication burden, single point of failure [10], privacy breaches [11] and so on. In contrast, Decentralized Federated Learning (DFL) centralizes both the local update and aggregation steps on the client, which offers enhanced privacy protection [12], faster model training [13], and robustness to slow client devices [14]. Therefore, DFL has become a popular alternative solution [10], [13]. Qinglun Li, Miao Zhang, and Quanjun Yin are with the College of Systems Engineering, National University of Defense Technology. Yingqi Liu, Li Shen, and Xiaochun Cao are with the School of Cy-ber Science and Technology, Shenzhen Campus of Sun Y at-sen University, Shenzhen 518107, China. The optimization process diagrams for two clients under the DFedAvg and DFedCata algorithms are simulated. The primary improvements include two aspects.