A review of federated learning in renewable energy applications:
  Potential, challenges, and future directions
Albin Grataloup Stefan Jonas Angela Meyer
Federated learning has recently emerged as a privacy-preserving distributed
machine learning approach. Federated learning enables collaborative training of
multiple clients and entire fleets without sharing the involved training
datasets. By preserving data privacy, federated learning has the potential to
overcome the lack of data sharing in the renewable energy sector which is
inhibiting innovation, research and development. Our paper provides an overview
of federated learning in renewable energy applications. We discuss federated
learning algorithms and survey their applications and case studies in renewable
energy generation and consumption. We also evaluate the potential and the
challenges associated with federated learning applied in power and energy
contexts. Finally, we outline promising future research directions in federated
learning for applications in renewable energy.