Composite federated learning with heterogeneous data
Jiaojiao Zhang Jiang Hu Mikael Johansson
We propose a novel algorithm for solving the composite Federated Learning
(FL) problem. This algorithm manages non-smooth regularization by strategically
decoupling the proximal operator and communication, and addresses client drift
without any assumptions about data similarity. Moreover, each worker uses local
updates to reduce the communication frequency with the server and transmits
only a $d$-dimensional vector per communication round. We prove that our
algorithm converges linearly to a neighborhood of the optimal solution and
demonstrate the superiority of our algorithm over state-of-the-art methods in
numerical experiments.