Staleness-Controlled Asynchronous Federated Learning: Accuracy and Efficiency Tradeoff

Federated Learning (FL) is an emerging distributed learning paradigm with the privacy-preserving advantage of collaboratively training a shared model across multiple participants. Considering the prevailing device heterogeneity circumstance in practice, asynchronous interaction is introduced into FL to break the straggler barrier of synchronization, at the cost of significant accuracy degradation derived from model staleness. Although quite a few works attempt to partially mitigate the detrimental impact after occurring staleness issue, they neglect to control the overall staleness degree of clients-side local models from the whole training perspective, resulting in highly-stale models for aggregation and slow convergence speed. To this end, we propose a Staleness-Controlled Asynchronous Federated Learning (SC-AFL) method, which enables to restrict staleness degree of local models within a certain bound via dynamically tuning the aggregated strategy of each round, aiming to strike a good balance between accuracy guarantee and convergence acceleration. Specifically, we leverage the Lyapunov optimization framework to decouple the troublesome round-coupling problem into the single-round sequential solving problem, and further develop a deterministic algorithm that selects the aggregated number of clients to minimize training time under the constraint of maintaining staleness queue stability. Besides, we derive the theoretical convergence analysis of SC-AFL and also present the upper bound of the performance gap with the optimum. Extensive experiments on three datasets demonstrate the superiority of SC-AFL in terms of time-to-accuracy speedup on both IID and Non-IID data distributions, achieving a good balance between model accuracy and convergence efficiency in AFL system.