Explicit Personalization and Local Training: Double Communication
  Acceleration in Federated Learning
Kai Yi Laurent Condat Peter Richtárik
Federated Learning is an evolving machine learning paradigm, in which
multiple clients perform computations based on their individual private data,
interspersed by communication with a remote server. A common strategy to
curtail communication costs is Local Training, which consists in performing
multiple local stochastic gradient descent steps between successive
communication rounds. However, the conventional approach to local training
overlooks the practical necessity for client-specific personalization, a
technique to tailor local models to individual needs. We introduce Scafflix, a
novel algorithm that efficiently integrates explicit personalization with local
training. This innovative approach benefits from these two techniques, thereby
achieving doubly accelerated communication, as we demonstrate both in theory
and practice.