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arXiv · 2506.13150

Federated ADMM from Bayesian Duality

Thomas Möllenhoff·Siddharth Swaroop·Finale Doshi-Velez·Mohammad Emtiyaz Khan
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Abstract

We propose a new Bayesian approach to generalize the federated Alternating Direction Method of Multipliers (ADMM). We show that the solutions of variational-Bayesian (VB) objectives are associated with a duality structure that not only resembles the structure of ADMM's fixed-points but also generalizes it. For example, ADMM-like updates are recovered when the VB objective is optimized over the isotropic-Gaussian family, and new non-trivial extensions are obtained for other exponential-family distributions. These extensions include a Newton-like variant that converges in one step on quadratic objectives and an Adam-like variant that yields up to 7% accuracy boosts for deep heterogeneous cases. Our work opens a new Bayesian way to generalize ADMM and other primal-dual methods.

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Thomas Möllenhoff, Siddharth Swaroop, Finale Doshi-Velez, Mohammad Emtiyaz Khan. 2025-06-16. Federated ADMM from Bayesian Duality. https://arxiv.org/abs/2506.13150

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