DAFL: Device-to-Device Transmissions for Delay-Efficient Federated Learning Over Mobile Devices

Huai-An Su, Pavana Prakash, Rui Chen, Yanmin Gong, Rong Yu, Xin Fu, Miao Pan

Abstract: Federated learning (FL) over mobile devices is an emerging distributed learning paradigm for numerous delay sensitive applications. In FL, the training delay is composed of the computing and communication delay. Some of the participating mobile devices may have slow local computing or wireless communications, which results in high FL training delay. Intuitively, if fast devices help slow ones, the FL training delay can potentially be reduced. However, helping each other among devices requires frequent transmissions and may cause additional delay. Fortunately, we observe that device-to-device (D2D) transmission, a fast and direct transmission, may be applied between device pairs to mitigate the additional delay from frequent transmissions. Inspired by those observations, we develop the D2D transmission assisted FL (DAFL), a novel FL scheme to improve the training delay over mobile devices. Briefly, we first put the eligible mobile devices into pairs, assigning each pair to one of the four types of relation: 1) similar computing, large communication gap; 2) similar communication, large computing gap; 3) one with faster computing and the other with faster communication; and 4) one with both faster computing and communication. We design the process for each type of device pair to: 1) improve the transmission delay of each pair, by letting the fast device help with the model parameters transmission to the server and 2) improve the computing delay by splitting learning task between paired devices. The emulation results demonstrate that DAFL surpasses existing peer designs in terms of reducing training delay by more than 20%.