Energy-Efficient Link Selection for Decentralized Learning via Smart Devices with Edge Computing
Cheng-Wei Ching, Chung-Kai Yang, Yu-Chun Liu, Chia‐Wei Hsu, Jian-Jhih Kuo, Hung-Sheng Huang, Jen-Feng Lee · 2020
Data privacy preservation has drawn much attention in emerging machine learning applications. Decentralized learning is thus developed to guarantee data security and get rid of the involvement of parameter server to avoid transmission bottleneck. However, the previous research focuses on data compression and exchange rules of model parameters among smart devices but neglects the interplay between link cardinality and transmission power consumption. To jointly optimize these issues, in this paper, we first formulate a new optimization problem, named GreenDL, prove its hardness, and then propose an approximation algorithm termed CoTRAIN. Experiment and simulation results manifest that CoTRAIN reduces more than 20% power compared with traditional methods without sacrificing the convergence rate.