Enhancing Split Federated Learning With Worker Clustering and Feature Compression

Mobile devices contribute more than half of the world’s web traffic, providing massive and diverse data for powering various computer vision (CV) applications, such as fall detection and image segmentation. To extract knowledge from remote devices without pulling their raw data, federated learning (FL) has emerged and usually works in a client-server fashion. To relax the computation burdens of training large-scale models on resource-constrained workers, we propose a novel split federated learning (SFL) framework with worker clustering and feature compression, termed ClusterSFL. All participating workers are supposed to train only the bottom or top submodel, and are arranged into multiple clusters. In each cluster, the workers collaboratively perform the SFL training procedure in parallel, while the PS only aggregates the cluster-wise entire models rather than massive features. However, considering the critical challenges encountered in on-device intelligent CV applications, such as communication resource limitation, statistical heterogeneity, and system heterogeneity, it is challenging to arrange suitable workers to specific clusters for efficient model training. Herein, we develop an effective worker clustering strategy and feature compression ratio configuration by optimizing the utility functions related to training efficiency and model accuracy. Specifically, ClusterSFL partitions workers with appropriate feature compression ratios into different clusters under the heterogeneity restrictions, thereby promoting model accuracy and training efficiency. Meanwhile, ClusterSFL adaptively assigns diverse local updating frequencies for each cluster to further address system heterogeneity. Extensive experiments are conducted on a physical platform with 80 NVIDIA Jetson devices, and the experimental results show that ClusterSFL reduces traffic consumption by about 22%~63%, speeds up model training by about 1.29× ~ 2.83×, and improves model accuracy by about 2%~27% in heterogeneous scenarios, compared to the baselines.