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\title{FedSPD: A Soft-clustering Approach \\ for Personalized Decentralized Federated Learning}

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% All author information is authomatically removed by the class for the
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\author[1]{\href{mailto:<ichengl@andrew.cmu.edu>?Subject=FedSPD: A Soft-clustering Approach for Personalized Decentralized Federated Learning}{I-Cheng Lin}{}}
\author[1]{Osman Ya\u{g}an}
\author[1]{Carlee Joe-Wong}
% Add affiliations after the authors
\affil[1]{%
    Department of Electrical \& Computer Engineering\\
    Carnegie Mellon University\\
    Pittsburgh, Pennsylvania, USA
}
  
  \begin{document}
\maketitle

\begin{abstract}
  Federated learning has recently gained popularity as a framework for distributed clients to collaboratively train a machine learning model using their 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 \textbf{\algname}, an efficient personalized federated learning algorithm for the decentralized setting, and show that it learns accurate models 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 \textbf{\algname}~outperforms multiple decentralized variants of existing personalized federated learning algorithms in scenarios with \textit{low-connectivity} networks.
\end{abstract}

\input{sections/introduction}

\input{sections/formulation}

\input{sections/algorithms}

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\input{sections/simulation_new}

\input{sections/conclusion}



\begin{acknowledgements} % will be removed in pdf for initial submission,
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    This work was supported in part by the National Science Foundation (NSF) under Grants CNS-2312761 and CNS-1751075, and by the Office of Naval Research (ONR) under Grant N00014-23-1-2275.
\end{acknowledgements}

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\title{Supplementary Material}
\maketitle

\appendix

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\end{document}
