  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 successful links is a highly
effective strategy for accelerating the system convergence.
