% \documentclass{uai2025} % for initial submission
\documentclass[accepted]{uai2025} % after acceptance, for a revised version; 
% also before submission to see how the non-anonymous paper would look like 
                        
%% There is a class option to choose the math font
% \documentclass[mathfont=ptmx]{uai2025} % ptmx math instead of Computer
                                         % Modern (has noticeable issues)
% \documentclass[mathfont=newtx]{uai2025} % newtx fonts (improves upon
                                          % ptmx; less tested, no support)
% NOTE: Only keep *one* line above as appropriate, as it will be replaced
%       automatically for papers to be published. Do not make any other
%       change above this note for an accepted version.

%% Choose your variant of English; be consistent
\usepackage[american]{babel}
% \usepackage[british]{babel}

%% Some suggested packages, as needed:
\usepackage{natbib} % has a nice set of citation styles and commands
    \bibliographystyle{plainnat}
    \renewcommand{\bibsection}{\subsubsection*{References}}
\usepackage{mathtools} % amsmath with fixes and additions
% \usepackage{siunitx} % for proper typesetting of numbers and units
\usepackage{booktabs} % commands to create good-looking tables
\usepackage{tikz} % nice language for creating drawings and diagrams






\usepackage{times}
\usepackage{soul}
\usepackage{url}
% \usepackage[hidelinks]{hyperref}
% \usepackage[utf8]{inputenc}
\usepackage{caption}
\usepackage{graphicx}
\usepackage{amsmath,amssymb}
\usepackage{amsthm}
\usepackage{booktabs}
\usepackage{algorithm}
\usepackage{algorithmic}
\usepackage[switch]{lineno}
\usepackage{natbib}
\usepackage{threeparttable}
\newcommand{\xw}[1]{\textcolor{blue}{[XW: #1]}}
\newcommand{\hq}[2]{\textcolor{red}{[HQ: #2]}}
\newcommand{\rev}[1]{\textcolor{red}{[rev: #1]}}
\usepackage{bm} 
\usepackage{colortbl}
\usepackage{subcaption}
\linenumbers
\urlstyle{same}
\newtheorem{example}{Example}
\newtheorem{theorem}{Theorem}
\newtheorem{remark}{Remark}
\newtheorem{lemma}{Lemma}
\newtheorem{corollary}{Corollary}



\newcommand{\review}[1]{\textbf{Question:} \emph{#1}}
\newcommand{\answer}[1]{\newline\textbf{Answer:} \textcolor{blue}{#1}}

%% Provided macros
% \smaller: Because the class footnote size is essentially LaTeX's \small,
%           redefining \footnotesize, we provide the original \footnotesize
%           using this macro.
%           (Use only sparingly, e.g., in drawings, as it is quite small.)

%% Self-defined macros
\newcommand{\swap}[3][-]{#3#1#2} % just an example

\title{Near-Optimal Regret Bounds for Federated Multi-armed Bandits \\ with Fully Distributed Communication}

% The standard author block has changed for UAI 2025 to provide
% more space for long author lists and allow for complex affiliations
%
% All author information is authomatically removed by the class for the
% anonymous submission version of your paper, so you can already add your
% information below.
%
% Add authors
% \author[1,4]{\href{mailto:<jj@example.edu>?Subject=Your UAI 2025 paper}{Haoran~Zhang}{}}
\author[1,4]{{Haoran~Zhang}}
\author[2]{Xuchuang~Wang}
\author[1,4]{Haoxu~Chen}
\author[3]{Hao~Qiu}
\author[1,4]{\href{mailto:linyang@nju.edu.cn}{Lin~Yang}{\thanks{Corresponding author: {linyang@nju.edu.cn}}}}
\author[1,4]{Yang~Gao}
% \author[3,1]{Further~Coauthor}
% Add affiliations after the authors
\affil[1]{%
    School of Intelligent Science and Technology\\
    Nanjing University\\
    Suzhou, China
}
\affil[2]{%
    College of Information \& Computer Science\\
    University of Massachusetts Amherst\\
    Massachusetts, USA
}
\affil[3]{%
    Dipartimento di Informatica\\
    Università degli Studi di Milano\\
    Milan, Italy
  }
\affil[4]{%
    National Key Laboratory for Novel Software Technology\\
    China
  }

  
\begin{document}
\maketitle

\begin{abstract}
In this paper, we focus on the research of federated multi-armed bandit (FMAB) problems where agents can only communicate with their neighbors. 
All agents aim to solve a common multi-armed bandit~(MAB) problem to minimize individual regrets, while group regret can also be minimized.
In a federated bandit problem, an agent fails to estimate the global reward means of arms by only using local observations, and hence, the bandit learning algorithm usually adopts a consensus estimation strategy to address the heterogeneity. 
However, up to now, the existing algorithms with fully distributed communication graphs only achieved a suboptimal result for the problem.
To address that, a fully distributed online consensus estimation algorithm (\texttt{CES}) is proposed to estimate the global mean without bias. 
Integrating this consensus estimator into a distributed successive elimination bandit algorithm framework yields our federated bandit algorithm. 
Our algorithm significantly improves both individual and group regrets over previous approaches, and we provide an in-depth analysis of the lower bound for this problem. 
\end{abstract}

\input{Section/Introduction}
\input{Section/Problem Formulation}
\input{Section/Algorithm}
\input{Section/Analysis}
\input{Section/Simulation}
\input{Section/Conclusion}


% References
\bibliography{uai2025-template}

\newpage

\onecolumn



\appendix
\title{Supplementary Material}
\maketitle
% \vspace{-4cm}
% This Supplementary Material should be submitted together with the main paper.

\input{Section/Appendix}
% \input{Section/Rebuttal}

\end{document}
