We tackle the problem of Federated Learning in the non i.i.d. case, in which local models drift apart, inhibiting learning. Building on an analogy with Lifelong Learning, we adapt a solution for catastrophic forgetting to Federated Learning. We add a penalty term to the loss function, compelling all local models to converge to a shared optimum. We show that this can be done efficiently for communication (adding no further privacy risks), scaling with the number of nodes in the distributed setting. Our experiments show that this method is superior to competing ones for image recognition on the MNIST dataset.
@article{arxiv.1910.07796,
title = {Overcoming Forgetting in Federated Learning on Non-IID Data},
author = {Neta Shoham and Tomer Avidor and Aviv Keren and Nadav Israel and Daniel Benditkis and Liron Mor-Yosef and Itai Zeitak},
journal= {arXiv preprint arXiv:1910.07796},
year = {2019}
}
Comments
Accepted to NeurIPS 2019 Workshop on Federated Learning for Data Privacy and Confidentiality