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\twocolumn[
\icmltitle{FedSPD: A Soft-clustering Approach for Personalized Decentralized Federated Learning}

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\begin{icmlauthorlist}
\icmlauthor{I-Cheng Lin}{cmu}
\icmlauthor{Osman Ya\u{g}an}{cmu}
\icmlauthor{Carlee Joe-Wong}{cmu}
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\icmlaffiliation{cmu}{Department of Electrical \& Computer Engineering, Carnegie Mellon University, Pittsburgh, PA, USA}
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\icmlcorrespondingauthor{I-Cheng Lin}{ichengl@andrew.cmu.edu}
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\icmlkeywords{Decentralized Federated Learning, Personalized Federated Learning}

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\begin{abstract}
Federated learning has recently gained popularity as a framework for distributed clients to collaboratively train a machine learning model using local data. While traditional federated learning relies on a central server for model aggregation, recent advancements adopt a decentralized framework, enabling direct model exchange between clients and eliminating the single point of failure. However, existing decentralized frameworks often assume all clients train a shared model. Personalizing each client's model can enhance performance, especially with heterogeneous client data distributions. We propose \algname, an efficient personalized federated learning algorithm for the decentralized setting, and show that it learns accurate models even in \textit{low-connectivity} networks. To provide theoretical guarantees on convergence, we introduce a clustering-based framework that enables consensus on models for distinct data clusters while personalizing to unique mixtures of these clusters at different clients. This flexibility, allowing selective model updates based on data distribution, substantially reduces communication costs compared to prior work on personalized federated learning in decentralized settings. Experimental results on real-world datasets show that \algname~outperforms multiple decentralized variants of personalized federated learning algorithms, especially in scenarios with \textit{low-connectivity} networks.
\end{abstract}

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% This document was modified from the file originally made available by
% Pat Langley and Andrea Danyluk for ICML-2K. This version was created
% by Iain Murray in 2018, and modified by Alexandre Bouchard in
% 2019 and 2021 and by Csaba Szepesvari, Gang Niu and Sivan Sabato in 2022.
% Modified again in 2023 and 2024 by Sivan Sabato and Jonathan Scarlett.
% Previous contributors include Dan Roy, Lise Getoor and Tobias
% Scheffer, which was slightly modified from the 2010 version by
% Thorsten Joachims & Johannes Fuernkranz, slightly modified from the
% 2009 version by Kiri Wagstaff and Sam Roweis's 2008 version, which is
% slightly modified from Prasad Tadepalli's 2007 version which is a
% lightly changed version of the previous year's version by Andrew
% Moore, which was in turn edited from those of Kristian Kersting and
% Codrina Lauth. Alex Smola contributed to the algorithmic style files.
