PAS: Towards Accurate and Efficient Federated Learning with Parameter-Adaptive Synchronization

Zuo Gan, Chen Chen, Jiayi Zhang, Gaoxiong Zeng, Yifei Zhu, Jieru Zhao, Quan Chen, Minyi Guo

Abstract: Federated Learning (FL) is a distributed paradigm that supports collaborated model training while preserving data privacy, where clients periodically synchronize their local gradients once after multiple local iterations. Due to non-uniform data distribution and poor network condition, FL processes often suffer degraded training accuracy and efficiency. In this work, we analyze the microscopic parameter variation behaviors in FL, and find that an effective method to improve FL accuracy is to switch to more frequent synchronization at proper moments. Moreover, such moments can be detected from gradient characteristics, and are heterogeneous across different parameters. Motivated by such observations, we propose Parameter-Adaptive Synchronization (PAS), a FL scheme that adaptively tunes the synchronization period for each scalar parameter. The benefits of PAS are two-fold: By switching to more frequent synchronization when necessary, we can improve the FL training accuracy; by synchronizing different parameters independently, we can enable communication-computation overlapping and enhance the network utilization. We implemented PAS atop PyTorch, and extensive experiments show that it can substantially improve FL performance in both accuracy and communication efficiency.