FedEFsz: Fair Cross-Silo Federated Learning System With Error-Bounded Lossy Compression

Zhaorui Zhang, Sheng Di, Benben Liu, Zhuoran Ji, Guanpeng Li, Xiaoyi Lu, Amelie Chi Zhou, Khalid Ayedh Alharthi, Jiannong Cao

Abstract: Cross-Silo federated learning systems have been identified as an efficient approach to scaling DNN training across geographically-distributed data silos to preserve the privacy of the training data. Communication efficiency and fairness are two major issues that need to be both satisfied when federated learning systems are deployed in practice. Simultaneously guaranteeing both of them, however, is exceptionally difficult because simply combining communication reduction and fairness optimization approaches often causes non-converged training or drastic accuracy degradation. To bridge this gap, we propose FedEFsz. On the one hand, it integrates the state-of-the-art error-bounded lossy compressor SZ3 into cross-silo federated learning systems to significantly reduce communication traffic during the training. On the other hand, it achieves a high fairness (i.e., rather consistent model accuracy and performance across different clients) through a carefully designed heuristic algorithm that can tune the error-bound of SZ3 for different clients during the training. Extensive experimental results based on a GPU cluster with 65 GPU cards show that FedEFsz improves the fairness across different benchmarks by up to 60.88% and meanwhile reduces the communication traffic by up to $315\times$.

External IDs:dblp:journals/tpds/ZhangDLJLLZAC25