Adaptive Distillation for Decentralized Learning from Heterogeneous Clients

18 Aug 2020Jiaxin MaRyo YonetaniZahid Iqbal

This paper addresses the problem of decentralized learning to achieve a high-performance global model by asking a group of clients to share local models pre-trained with their own data resources. We are particularly interested in a specific case where both the client model architectures and data distributions are diverse, which makes it nontrivial to adopt conventional approaches such as Federated Learning and network co-distillation... (read more)

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