  Federated Learning (FL) is an emerging machine learning technique that
enables distributed model training across data silos or edge devices without
data sharing. Yet, FL inevitably introduces inefficiencies compared to
centralized model training, which will further increase the already high energy
usage and associated carbon emissions of machine learning in the future. One
idea to reduce FL's carbon footprint is to schedule training jobs based on the
availability of renewable excess energy that can occur at certain times and
places in the grid. However, in the presence of such volatile and unreliable
resources, existing FL schedulers cannot always ensure fast, efficient, and
fair training.
  We propose FedZero, an FL system that operates exclusively on renewable
excess energy and spare capacity of compute infrastructure to effectively
reduce a training's operational carbon emissions to zero. Using energy and load
forecasts, FedZero leverages the spatio-temporal availability of excess
resources by selecting clients for fast convergence and fair participation. Our
evaluation, based on real solar and load traces, shows that FedZero converges
significantly faster than existing approaches under the mentioned constraints
while consuming less energy. Furthermore, it is robust to forecasting errors
and scalable to tens of thousands of clients.
