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Mobile edge learning is an emerging technique that enables distributed edgedevices to collaborate in training shared machine learning models by exploitingtheir local data samples and communication and computation resources. To dealwith the straggler dilemma issue faced in this technique, this paper proposes anew device to device enabled data sharing approach, in which different edgedevices share their data samples among each other over communication links, inorder to properly adjust their computation loads for increasing the trainingspeed. Under this setup, we optimize the radio resource allocation for bothdata sharing and distributed training, with the objective of minimizing thetotal training delay under fixed numbers of local and global iterations.Numerical results show that the proposed data sharing design significantlyreduces the training delay, and also enhances the training accuracy when thedata samples are non independent and identically distributed among edgedevices.

D2D-Enabled Data Sharing for Distributed Machine Learning at Wireless Network Edge