Efficient Asynchronous Federated Edge Learning Oriented Tasks Scheduling and Resources Allocation in Dynamic Multitasks MEC Networks

Asynchronous federated edge learning (Asy-FEEL) has drawn intensive attention due to its ability to effectively address the straggler issue caused by the heterogeneity of the participated mobile devices (MDs). The quality of Asy-FEEL depends highly on the number of participating MDs. Since the local training of federated learning consumes computation resources of MDs, it inevitably reduces the resources that can be devoted to their own tasks (OTs). Therefore, there is always a decreased incentive of MDs to participate in Asy-FEEL, subsequently reducing the amount of Asy-FEEL tasks executed by MDs, thereby failing in achieving satisfied Asy-FEEL performance. How to effectively utilize the limited resources and schedule Asy-FEEL tasks and OTs to satisfy the quality of service requirements on the OTs and at the same time encourage MDs to participate to execute Asy-FEEL tasks is of vital importance. To this end, a joint tasks scheduling and resource allocation problem is formulated and investigated within a dynamic multitasks mobile edge computing (MEC) network, where Asy-FEEL tasks and OTs coexist. Since the problem is a dynamic stochastic optimization problem, a Lyapunov-based dynamic joint tasks scheduling and resources allocation (Lya-DJTR) algorithm is proposed to determine tasks scheduling, computational resource and bandwidth allocation, and transmit power control at MDs simultaneously. Simulation results demonstrate the superiority of the proposed algorithm in improving the efficiency of Asy-FEEL while ensuring the real-time processing of OTs when compared to baseline algorithms.