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Decentralized learning with private data is a central problem in machinelearning. We propose a novel distillation-based decentralized learningtechnique that allows multiple agents with private non-iid data to learn fromeach other, without having to share their data, weights or weight updates. Ourapproach is communication efficient, utilizes an unlabeled public dataset anduses multiple auxiliary heads for each client, greatly improving trainingefficiency in the case of heterogeneous data. This approach allows individualmodels to preserve and enhance performance on their private tasks while alsodramatically improving their performance on the global aggregated datadistribution. We study the effects of data and model architecture heterogeneityand the impact of the underlying communication graph topology on learningefficiency and show that our agents can significantly improve their performancecompared to learning in isolation.

Decentralized Learning with Multi-Headed Distillation