Federated Variational Preference Alignment with Gumbel-Softmax Prior for Personalized User Preferences

• URL: http://arxiv.org/abs/2605.30873v1
• Date: Fri, 29 May 2026 05:52:21 GMT
• Title: Federated Variational Preference Alignment with Gumbel-Softmax Prior for Personalized User Preferences
• Abstract summary: We propose Federated Variational Preference Alignment with Gumbel-Softmax Prior (FedVPA-GP)<n>FedVPA-GP is a framework designed to disentangle diverse preferences without compromising privacy.<n> Experiments on the HH-RLHF dataset demonstrate that FedVPA-GP significantly outperforms monolithic baselines.
• Score: 27.034832184399992
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Federated Learning (FL) offers a privacy-preserving pathway for aligning Large Language Models (LLMs); however, existing frameworks typically enforce a monolithic reward model, inevitably averaging out inherently conflicting user preferences (e.g., helpfulness vs. harmlessness). While Variational Preference Learning (VPL) offers a pathway to personalization, adapting it to decentralized settings presents a fundamental challenge: posterior collapse driven by severe local data scarcity and heterogeneity. In this paper, we propose Federated Variational Preference Alignment with Gumbel-Softmax Prior (FedVPA-GP), a framework designed to disentangle diverse preferences without compromising privacy. To stabilize variational inference, we introduce a Federated Mixture Prior that enables clients to leverage the aggregate population distribution as a dynamic prior. Furthermore, we incorporate an Orthogonal Loss that explicitly enforces the separation of preference prototypes in the latent space. Experiments on the HH-RLHF dataset demonstrate that FedVPA-GP significantly outperforms monolithic baselines, successfully disentangling conflicting user intents and enabling dynamic preference switching.

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