Efficient Multi-Task Asynchronous Federated Learning in Edge Computing

Driven by the continuous upgrading of hardware devices, a notable shift from traditional single-FL task to complicated multi-FL tasks is emerging in edge computing, supporting richer intelligent services. With the presence of high capacity heterogeneity and intensive resource contention at the edge, it is highly non-trivial to collaboratively optimize resource scheduling of multiple FL tasks with diverse QoS requirements. To well tackle the above challenge, we firstly propose a novel Multi-Task Asynchronous Federated Learning (MTAFL) architecture. This novel framework has the potential to enhance resource utilization efficiency by enabling the orthogonal multiplexing of computation and communication resources through adjusting the number of local epochs on edge clients. Then, we formulate an optimization problem within the MTAFL framework, aiming at managing resource allocation and client scheduling to minimize the system-wide energy consumption while achieving the target FL performance. However, intricate couplings for resource allocation and local training decisions arise during the long-term FL process. We hence employ a two-step relaxation approach to transform original non-convex problem into a multi-convex problem, and further devise an efficient optimization strategy based on the block coordinate descent algorithm. Extensive numerical evaluations corroborate the superior performance of the proposed MTAFL framework over existing schemes.