Adapt to Adaptation: Learning Personalization for Cross-Silo Federated Learning. (arXiv:2110.08394v2 [cs.LG] UPDATED)

Conventional federated learning (FL) trains one global model for a federation
of clients with decentralized data, reducing the privacy risk of centralized
training. However, the distribution shift across non-IID datasets, often poses
a challenge to this one-model-fits-all solution. Personalized FL aims to
mitigate this issue systematically. In this work, we propose APPLE, a
personalized cross-silo FL framework that adaptively learns how much each
client can benefit from other clients' models. We also introduce a method to
flexibly control the focus …

arxiv federated learning learning personalization