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ARXIV:2605.30873 · FEDERATED LLM ALIGNMENT · SUBMITTED 01 JUN · 20:26 UTC · FRESHNESS STALE
ARXIV:2605.30873FEDERATED LLM ALIGNMENTSUBMITTED 01 JUN · 20:26 UTCFRESHNESS STALEJabin Koo · Hoyoung Kim · Minwoo Jang · Jungseul Ok · arXiv
A federated learning framework for personalized LLM alignment that disentangles diverse user preferences while preserving privacy.
Opportunity summary
Pain A federated learning framework for personalized LLM alignment that disentangles diverse user preferences while preserving privacy.
Evidence 0 refs | 3 sources | 50% coverage
Blocker Evidence unverified
A federated learning framework for personalized LLM alignment that disentangles diverse user preferences while preserving privacy. harmlessness).
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).
ScienceToStartup currently rates this 5.0/10 on the public viability pass. To stabilize variational inference, we introduce a Federated Mixture Prior that enables clients to leverage the aggregate population distribution as a dynamic prior. Code…
Federated LLM Alignment moved forward this cycle; last verified June 2026. Public score 5.0/10. Production flags indicate code availability.
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A federated learning framework for personalized LLM alignment that disentangles diverse user preferences while preserving privacy.
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10.48550/arXiv.2605.30873A federated learning framework for personalized LLM alignment that disentangles diverse user preferences while preserving privacy.
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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PROBLEM
A federated learning framework for personalized LLM alignment that disentangles diverse user preferences while preserving privacy. harmlessness).
METHOD
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).
RESULT
ScienceToStartup currently rates this 5.0/10 on the public viability pass. To stabilize variational inference, we introduce a Federated Mixture Prior that enables clients to leverage the aggregate population distribution as a dynamic prior. Code availability is flagged in the pr...
WHY NOW
Federated LLM Alignment moved forward this cycle; last verified June 2026. Public score 5.0/10. Production flags indicate code availability.
{"file name": "input.pdf", "number of pages": 21, "author": "Jabin Koo; Hoyoung Kim; Minwoo Jang; Jungseul Ok"
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A federated learning framework for personalized LLM alignment that disentangles diverse user preferences while preserving privacy.
Segment
Federated LLM Alignment
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Commercial read
5.0/10 public viability
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