  In this work, we examine a network of agents operating asynchronously, aiming
to discover an ideal global model that suits individual local datasets. Our
assumption is that each agent independently chooses when to participate
throughout the algorithm and the specific subset of its neighbourhood with
which it will cooperate at any given moment. When an agent chooses to take
part, it undergoes multiple local updates before conveying its outcomes to the
sub-sampled neighbourhood. Under this setup, we prove that the resulting
asynchronous diffusion strategy is stable in the mean-square error sense and
provide performance guarantees specifically for the federated learning setting.
We illustrate the findings with numerical simulations.
