Joint Link Scheduling and Resource Allocation for Hierarchical Asynchronous Deep Mutual Learning System
Tingli Wang, Shengli Liu, Jiantao Yuan, Xianfu Chen, Celimuge Wu, Rui Yin · 2024
Deep mutual learning (DML) is one of the emerging technologies for mobile intelligent applications that has attracted much attention in recent years. To effectively deploy DML at a large scale, in this paper, we propose a novel hierarchical asynchronous deep mutual learning (HADML) system that enables devices to collaborate in model training without the exchange of local datasets. To further improve the learning efficiency, the average energy cost for model exchanging is minimized by jointly optimizing the link scheduling and communication resource allocation. To efficiently solve this problem, the graph neural network and deep unfolding network are employed to obtain the link scheduling and resource allocation, respectively. Finally, the simulation results demonstrate that our proposed algorithm can achieve a balance between knowledge sharing and communication energy consumption.