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\begin{document}

\twocolumn[
\mlsystitle{D-Cliques: Compensating for Data Heterogeneity with Topology in
Decentralized Federated Learning}

% It is OKAY to include author information, even for blind
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\begin{mlsysauthorlist}
\mlsysauthor{Aur\'elien Bellet}{inria-lille}
\mlsysauthor{Anne-Marie Kermarrec}{epfl}
\mlsysauthor{Erick Lavoie}{epfl}
\end{mlsysauthorlist}

\mlsysaffiliation{epfl}{EPFL, Lausanne, Switzerland}
\mlsysaffiliation{inria-lille}{Inria, Lille, France}

\mlsyscorrespondingauthor{Erick Lavoie}{erick.lavoie@epfl.ch}

% You may provide any keywords that you
% find helpful for describing your paper; these are used to populate
% the "keywords" metadata in the PDF but will not be shown in the document
\mlsyskeywords{Decentralized Learning, Federated Learning, Topology,
Non-IID Data, Stochastic Gradient Descent}

\vskip 0.3in

\begin{abstract}
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%Abstracts must be a single paragraph, ideally between 4--6 sentences long.
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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 empirical evaluation on MNIST and CIFAR10 demonstrates that our approach
provides similar convergence speed as a fully-connected topology with a
significant reduction in the number of edges and messages. In a 1000-node
topology, D-Cliques requires 98\% less edges and 96\% less total messages,
with further possible gains using a small-world topology across cliques.
\end{abstract}
]

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\input{intro}
\input{setting}
\input{d-cliques}
\input{exp}
\input{related_work}
\input{conclu}

\bibliography{main.bib}
\bibliographystyle{mlsys2022}

\input{appendix}

\end{document}


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