An Energy-Efficient Deep Mutual Learning System Based on D2D-U Communications

<italic>Deep mutual learning</italic> (DML) is one of the most high-profile technologies emerging in the field of machine learning during the past few years. DML has the potential of exchanging knowledge on the premise of ensuring data privacy, while retaining the characteristics of local models. In this paper, we design a novel system named as <italic>decentralized mutual learning over unlicensed spectrum</italic> (DML-U), which allows neighbor mobile devices to learn from each other via bidirectional <italic>device-to-device links over unlicensed spectrum</italic> (D2D-U). On this basis, we formulate a non-convex optimization problem for the one-to-one pairing scenario with the goal of minimizing the average communication energy cost for sharing knowledge. We further propose a two-layer iterative algorithm that includes the outer layer based on the enumeration method and the inner layer based on the sum-of-ratios optimization, aiming to find the optimal pairing scheme between devices and obtain the global optimal communication resource allocation scheme, respectively. The numerical results validate the effectiveness of the proposed algorithm in improving the DML performance.