Offloading Federated Learning Task to Edge Computing with Trust Execution Environment

Abstract

Federated Learning (FL) takes advantage of distributed data to jointly train a global deep learning model on many clients, without revealing local data to the central server for privacy guarantee. However, due to the heterogeneity of the FL clients, some poor performance clients may become stragglers, impeding the global training process. It is desirable to offload these stragglers’ tasks to some high performance servers, but this is at the risk of data privacy leakage. To mitigate such problem, we introduce the edge servers empowered by Trusted Execution Environment (TEE) to securely help the FL clients with poor performance. With the consideration of limited computation resource in TEE, we further investigate how to select the clients for help. Considering the time-varying processing capabilities on the FL clients, we propose an exploration-exploitation based client selection algorithm. Via evaluating our algorithm in a practical FL training task, the experiments show that the proposed algorithm indeed accelerate training process thanks to its efficient client selection.