  Catering to the proliferation of Internet of Things devices and distributed
machine learning at the edge, we propose an energy harvesting federated
learning (EHFL) framework in this paper. The introduction of EH implies that a
client's availability to participate in any FL round cannot be guaranteed,
which complicates the theoretical analysis. We derive novel convergence bounds
that capture the impact of time-varying device availabilities due to the random
EH characteristics of the participating clients, for both parallel and local
stochastic gradient descent (SGD) with non-convex loss functions. The results
suggest that having a uniform client scheduling that maximizes the minimum
number of clients throughout the FL process is desirable, which is further
corroborated by the numerical experiments using a real-world FL task and a
state-of-the-art EH scheduler.
