An Operator Splitting View of Federated Learning

• URL: http://arxiv.org/abs/2108.05974v2
• Date: Fri, 29 Nov 2024 18:54:31 GMT
• Title: An Operator Splitting View of Federated Learning
• Authors: Saber Malekmohammadi, Kiarash Shaloudegi, Zeou Hu, Yaoliang Yu,
• Abstract summary: In the past few years, the learning ($texttFL$) community has witnessed a proliferation of new $texttFL$ algorithms. We compare different algorithms with ease, to previous convergence results and to uncover new algorithmic variants. The unification algorithms also leads a way to accelerate $texttFL$ algorithms, without any overhead.
• Score: 15.968575321322225
• License:
• Abstract: Over the past few years, the federated learning ($\texttt{FL}$) community has witnessed a proliferation of new $\texttt{FL}$ algorithms. However, our understating of the theory of $\texttt{FL}$ is still fragmented, and a thorough, formal comparison of these algorithms remains elusive. Motivated by this gap, we show that many of the existing $\texttt{FL}$ algorithms can be understood from an operator splitting point of view. This unification allows us to compare different algorithms with ease, to refine previous convergence results and to uncover new algorithmic variants. In particular, our analysis reveals the vital role played by the step size in $\texttt{FL}$ algorithms. The unification also leads to a streamlined and economic way to accelerate $\texttt{FL}$ algorithms, without incurring any communication overhead. We perform numerical experiments on both convex and nonconvex models to validate our findings.

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