D-Cliques: Compensating for Data Heterogeneity with Topology in Decentralized Federated Learning

TL;DR

This paper introduces D-Cliques, a topology design for decentralized federated learning that reduces data heterogeneity effects by grouping nodes into representative cliques, improving convergence speed and communication efficiency.

Contribution

The paper proposes D-Cliques, a novel topology that mitigates label distribution skew in decentralized federated learning, with an adaptive gradient update method and empirical validation.

Findings

D-Cliques achieve similar convergence as fully-connected topologies.

Significant reduction in communication overhead with 98% fewer edges.

Effective in heterogeneous data settings on MNIST and CIFAR10.

Abstract

The convergence speed of machine learning models trained with Federated Learning is significantly affected by heterogeneous data partitions, even more so in a fully decentralized setting without a central server. In this paper, we show that the impact of label distribution skew, an important type of data heterogeneity, can be significantly reduced by carefully designing the underlying communication topology. We present D-Cliques, a novel topology that reduces gradient bias by grouping nodes in sparsely interconnected cliques such that the label distribution in a clique is representative of the global label distribution. We also show how to adapt the updates of decentralized SGD to obtain unbiased gradients and implement an effective momentum with D-Cliques. Our extensive empirical evaluation on MNIST and CIFAR10 demonstrates that our approach provides similar convergence speed as a fully-connected topology, which provides the best convergence in a data heterogeneous setting, with a significant reduction in the number of edges and messages. In a 1000-node topology, D-Cliques require 98% less edges and 96% less total messages, with further possible gains using a small-world topology across cliques.