Adaptive Control of Local Updating and Model Compression for Efficient Federated Learning
Yang Xu, Yunming Liao, Hongli Xu, Zhenguo Ma, Lun Wang, Jianchun Liu · IEEE Transactions on Mobile Computing · 2022
Data generated at the network edge can be processed locally by leveraging the paradigm of Edge Computing (EC). Aided by EC, Federated Learning (FL) has been becoming a practical and popular approach for distributed machine learning over locally distributed data. However, FL faces three critical challenges, i.e., resource constraint, system heterogeneity and context dynamics in EC. To address these challenges, we present a training-efficient FL method, termedFedLamp, by optimizing both theLocal updating frequency andmodel compression ratio in the resource-constrained EC systems. We theoretically analyze the model convergence rate and obtain a convergence upper bound related to the local updating frequency and model compression ratio. Upon the convergence bound, we propose a control algorithm, that adaptively determines diverse and appropriate local updating frequencies and model compression ratios for different edge nodes, so as to reduce the waiting time and enhance the training efficiency. We evaluate the performance ofFedLampthrough extensive simulation and testbed experiments. Evaluation results show thatFedLampcan reduce the traffic consumption by 63% and the completion time by about 52% for achieving the similar test accuracy, compared to the baselines.