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The current state-of-the-art decentralized learning algorithms mostly assumethe data distribution to be Independent and Identically Distributed (IID).However, in practical scenarios, the distributed datasets can havesignificantly heterogeneous data distributions across the agents. In this work,we present a novel approach for decentralized learning on heterogeneous data,where data-free knowledge distillation through contrastive loss oncross-features is utilized to improve performance. Cross-features for a pair ofneighboring agents are the features (i.e., last hidden layer activations)obtained from the data of an agent with respect to the model parameters of theother agent. We demonstrate the effectiveness of the proposed technique throughan exhaustive set of experiments on various Computer Vision datasets (CIFAR-10,CIFAR-100, Fashion MNIST, Imagenette, and ImageNet), model architectures, andnetwork topologies. Our experiments show that the proposed method achievessuperior performance (0.2-4% improvement in test accuracy) compared to otherexisting techniques for decentralized learning on heterogeneous data.

Cross-feature Contrastive Loss for Decentralized Deep Learning on Heterogeneous Data