Personalized Federated Learning: A Unified Framework and Universal Optimization Techniques

Understand

We investigate the optimization aspects of personalized Federated Learning (FL).

• We propose general optimizers that can be applied to numerous existing personalized FL objectives, specifically a tailored variant of Local SGD and variants of accelerated coordinate descent/accelerated SVRCD.
• By examining a general personalized objective capable of recovering many existing personalized FL objectives as special cases, we develop a comprehensive optimization theory applicable to a wide range of strongly convex personalized FL models in the literature.
• We showcase the practicality and/or optimality of our methods in terms of communication and local computation.