Efficient Federated Meta-Learning Over Multi-Access Wireless Networks

Federated meta-learning (FML) has emerged as a promising paradigm to cope with the data limitation and heterogeneity challenges in today’s edge learning arena. However, its performance is often limited by slow convergence and corresponding low communication efficiency. Besides, since the wireless bandwidth and IoT devices’ energy capacity are usually insufficient, it is crucial to control the resource allocation and energy consumption when deploying FML in realistic wireless networks. To overcome these challenges, in this paper, we first rigorously analyze each device’s contribution to the global loss reduction in each round and develop an FML algorithm (called NUFM) with a non-uniform device selection scheme to accelerate the convergence. After that, we formulate a resource allocation problem integrating NUFM in multi-access wireless systems to jointly improve the convergence rate and minimize the wallclock time along with energy cost. By deconstructing the original problem step by step, we devise a joint device selection and resource allocation strategy (called URAL) to solve the problem, and provide theoretical guarantees. Further, we show that the computational complexity of NUFM can be reduced from O (d2) to O (d) (with d being the model dimension) via combining two first-order approximation techniques. Extensive simulation results demonstrate the effectiveness and superiority of the proposed methods by comparing to the existing baselines.