Decentralized Learning over Wireless Networks: The Effect of Broadcast with Random Access. (arXiv:2305.07368v1 [cs.NI])

In this work, we focus on the communication aspect of decentralized learning,
which involves multiple agents training a shared machine learning model using
decentralized stochastic gradient descent (D-SGD) over distributed data. In
particular, we investigate the impact of broadcast transmission and
probabilistic random access policy on the convergence performance of D-SGD,
considering the broadcast nature of wireless channels and the link dynamics in
the communication topology. Our results demonstrate that optimizing the access
probability to maximize the expected number of …

agents arxiv broadcast communication data decentralized distributed distributed data focus gradient impact machine machine learning machine learning model multiple networks policy random stochastic training wireless work