Different from the above prior works focusing on the ML strategy or structure design for improving the communication performance, this letter proposes to employ the emerging communication technique, namely the device-to-device (D2D) communications (see, e.g., [10, 11]), to relieve the "straggler's dilemma" issue for improving the performance of distributed ML-model training. Recently, the D2D communications have been recognized as one key technique in fifth-generation (5G) and beyond cellular networks, in which wireless devices in close proximity can directly communicate with each other without going through cellular infrastructures such as base stations (BSs). Motivated by this, we propose a new D2D-enabled data sharing design for mobile edge learning, which allows edge devices to share their data samples over D2D communication links. By properly controlling the amounts of data samples exchanged, this design can not only adjust the computation loads at devices for enhancing the training speed, but also reshape the data distribution (if data samples at edge devices are non-IID) for enhancing the training accuracy. In particular, we aim to minimize the total delay for the ML-model training under fixed numbers of local and global iterations (for training), by optimizing the radio resource allocation for both D2D data sharing and distributed model training.

Abstract--Mobile edge learning is an emerging technique that enables distributed edge devices to collaborate in trainin g shared machine learning (ML) models by exploiting their local data samples and communication/computation resources. T o deal with the stragglers dilemma issue faced in this technique, this p aper proposes a new device-to-device (D2D)-enabled data sharin g approach, in which different edge devices share their data samples among each other over D2D communication links, in order to properly adjust their computation loads for increa sing the training speed. Under this setup, we optimize the radio resource allocation for both D2D-enabled data sharing and distributed training, with the objective of minimizing the total training delay under fixed numbers of local and global iterat ions (for training). Numerical results show that the proposed D2 D-enabled data sharing design significantly reduces the train ing delay, and also enhances the training accuracy when the data samples are non-independent and identically distributed ( non-IID) among edge devices. Mobile edge learning has recently attracted growing research interests from both academia and industry to enable various new artificial intelligence (AI) applications such as augmented reality (AR), industrial automation, and autonomous driving [1].