  Federated learning (FL) enables workers to learn a model collaboratively by
using their local data, with the help of a parameter server (PS) for global
model aggregation. The high communication cost for periodic model updates and
the non-independent and identically distributed (i.i.d.) data become major
bottlenecks for FL. In this work, we consider analog aggregation to scale down
the communication cost with respect to the number of workers, and introduce
data redundancy to the system to deal with non-i.i.d. data. We propose an
online energy-aware dynamic worker scheduling policy, which maximizes the
average number of workers scheduled for gradient update at each iteration under
a long-term energy constraint, and analyze its performance based on Lyapunov
optimization. Experiments using MNIST dataset show that, for non-i.i.d. data,
doubling data storage can improve the accuracy by 9.8% under a stringent energy
budget, while the proposed policy can achieve close-to-optimal accuracy without
violating the energy constraint.
