ContextFL: Context-aware Federated Learning by Estimating the Training and Reporting Phases of Mobile Clients

Abstract

Federated Learning (FL) suffers from Low-quality model training in mobile edge computing, due to the dynamic environment of mobile clients. To the best of our knowledge, most FL frameworks follow the reactive client scheduling, in which the FL parameter server selects participants according to the currently-observed state of clients. Thus, the participants selected by the reactive-manner methods are very likely to fail while training a round of FL. To this end, we propose a proactive Context-aware Federated Learning (ContextFL) mechanism, which consists of two primary modules. Firstly, the state prediction module enables each client device to predict the conditions of both local training and reporting phases of FL locally. Secondly, the decision-making algorithm module is devised using the contextual Multi-Armed Bandit (cMAB) framework, which can help the parameter server select the most appropriate group of mobile clients. Finally, we carried out trace-driven FL experiments using real-world mobility datasets collected from volunteers. The evaluation results demonstrate that the proposed ContextFL mechanism outperforms other baselines in terms of the convergence stability of the global FL model and the ratio of valid participants.