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\begin{abstract}
The convergence speed of machine learning models trained with Federated Learning is significantly affected by non-identically and independently distributed (non-IID) data partitions, even more so in a fully decentralized (serverless) setting. We propose the D-Cliques topology, which reduces gradient bias by grouping nodes in cliques such that their local joint distribution is representative of the global distribution. D-Cliques provide similar convergence speed as a fully-connected topology, both in IID and non-IID settings, with a significant reduction in the number of required edges and messages: at a scale of 1000 nodes, 98\% less edges and 96\% less total messages. We show how D-Cliques can be used to successfully implement momentum, critical to quickly train deep convolutional networks but otherwise detrimental in a non-IID setting. We finally show that, among many possible inter-clique topologies, a small-world topology that scales the number of edges logarithmically in the number of nodes this provides a further 22\% reduction in the number of edges compared to fully connecting cliques with a single edge pairwise at 1000 nodes, and suggests bigger possible gains at larger scales.
The convergence speed of machine learning models trained with Federated Learning is significantly affected by non-identically and independently distributed (non-IID) data partitions, even more so in a fully decentralized (serverless) setting. We propose the D-Cliques topology, which reduces gradient bias by grouping nodes in cliques such that their local joint distribution is representative of the global distribution. D-Cliques provide similar convergence speed as a fully-connected topology, both in IID and non-IID settings, with a significant reduction in the number of required edges and messages: at a scale of 1000 nodes, 98\% less edges and 96\% less total messages. We show how D-Cliques can be used to successfully implement momentum, critical to quickly train deep convolutional networks but otherwise detrimental in a non-IID setting. We finally show that, among many possible inter-clique topologies, a small-world topology that scales the number of edges logarithmically in the number of nodes provides a further 22\% reduction in the number of edges compared to fully connecting cliques with a single edge pairwise at 1000 nodes, and suggests bigger possible gains at larger scales.
