Efficient Multitask Asynchronous Federated Learning in Edge Computing: A Two-Layer Optimization Approach
Hui Lan Jiang, Xinyuan Cao, Tao Ouyang, Min Lin, Kongyange Zhao, Xiaodong Zhang, Xu Chen · IEEE Internet of Things Journal · 2025
Advances in hardware and AI have enabled edgebased IoT devices to leverage substantial computational and data resources, facilitating large-scale deployment of AI models, particularly through federated learning (FL). However, the high heterogeneity of devices and resource contention at the edge make collaborative optimization of resource scheduling for multiple FL tasks challenging. To tackle this, we propose a novel Multi-Task Asynchronous Federated Learning (MTAFL) architecture, which enhances resource utilization efficiency by enabling orthogonal multiplexing of computation and communication resources through adjusting local epochs on edge devices. Then, we formulate an optimization problem in the MTAFL framework to manage resources and local epochs, aiming to minimize energy consumption while achieving FL performance. However, intricate couplings between resource allocation and local control complicate the long-term FL process. To address this, we employ a two-step relaxation approach and develop an efficient optimization strategy based on the block coordinate descent algorithm. To enhance optimization granularity, we extend the MTAFL framework by incorporating device-level data characteristics. We propose a Gaussian Process-based client selection mechanism that dynamically characterizes and predicts training loss trajectories across clients. After selecting clients for each task, we optimize resource allocation and local control strategies in the system. Extensive numerical evaluations corroborate the superior performance of the proposed approaches over existing schemes.