  We consider federated edge learning (FEEL) among mobile devices that harvest
the required energy from their surroundings, and share their updates with the
parameter server (PS) through a shared wireless channel. In particular, we
consider energy harvesting FL with over-the-air (OTA) aggregation, where the
participating devices perform local computations and wireless transmission only
when they have the required energy available, and transmit the local updates
simultaneously over the same channel bandwidth. In order to prevent bias among
heterogeneous devices, we utilize a weighted averaging with respect to their
latest energy arrivals and data cardinalities. We provide a convergence
analysis and carry out numerical experiments with different energy arrival
profiles, which show that even though the proposed scheme is robust against
devices with heterogeneous energy arrivals in error-free scenarios, we observe
a 5-10% performance loss in energy harvesting OTA FL.
