  We present Epidemic Learning (EL), a simple yet powerful decentralized
learning (DL) algorithm that leverages changing communication topologies to
achieve faster model convergence compared to conventional DL approaches. At
each round of EL, each node sends its model updates to a random sample of $s$
other nodes (in a system of $n$ nodes). We provide an extensive theoretical
analysis of EL, demonstrating that its changing topology culminates in superior
convergence properties compared to the state-of-the-art (static and dynamic)
topologies. Considering smooth non-convex loss functions, the number of
transient iterations for EL, i.e., the rounds required to achieve asymptotic
linear speedup, is in $O(n^3/s^2)$ which outperforms the best-known bound
$O(n^3)$ by a factor of $s^2$, indicating the benefit of randomized
communication for DL. We empirically evaluate EL in a 96-node network and
compare its performance with state-of-the-art DL approaches. Our results
illustrate that EL converges up to $ 1.7\times$ quicker than baseline DL
algorithms and attains $2.2 $\% higher accuracy for the same communication
volume.
