  Institutions in highly regulated domains such as finance and healthcare often
have restrictive rules around data sharing. Federated learning is a distributed
learning framework that enables multi-institutional collaborations on
decentralized data with improved protection for each collaborator's data
privacy. In this paper, we propose a communication-efficient scheme for
decentralized federated learning called ProxyFL, or proxy-based federated
learning. Each participant in ProxyFL maintains two models, a private model,
and a publicly shared proxy model designed to protect the participant's
privacy. Proxy models allow efficient information exchange among participants
without the need of a centralized server. The proposed method eliminates a
significant limitation of canonical federated learning by allowing model
heterogeneity; each participant can have a private model with any architecture.
Furthermore, our protocol for communication by proxy leads to stronger privacy
guarantees using differential privacy analysis. Experiments on popular image
datasets, and a cancer diagnostic problem using high-quality gigapixel
histology whole slide images, show that ProxyFL can outperform existing
alternatives with much less communication overhead and stronger privacy.
