PluralLLM: Pluralistic Alignment in LLMs via Federated Learning

Mahmoud Srewa; Tianyu Zhao; Salma Elmalaki

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arXiv · 2503.09925

PluralLLM: Pluralistic Alignment in LLMs via Federated Learning

Mahmoud Srewa·Tianyu Zhao·Salma Elmalaki

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Abstract

Ensuring Large Language Models (LLMs) align with diverse human preferences while preserving privacy and fairness remains a challenge. Existing methods, such as Reinforcement Learning from Human Feedback (RLHF), rely on centralized data collection, making them computationally expensive and privacy-invasive. We introduce PluralLLM a federated learning-based approach that enables multiple user groups to collaboratively train a transformer-based preference predictor without sharing sensitive data, which can also serve as a reward model for aligning LLMs. Our method leverages Federated Averaging (FedAvg) to aggregate preference updates efficiently, achieving 46% faster convergence, a 4% improvement in alignment scores, and nearly the same group fairness measure as in centralized training. Evaluated on a Q/A preference alignment task, PluralLLM demonstrates that federated preference learning offers a scalable and privacy-preserving alternative for aligning LLMs with diverse human values.

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Mahmoud Srewa, Tianyu Zhao, Salma Elmalaki. 2025-03-13. PluralLLM: Pluralistic Alignment in LLMs via Federated Learning. https://arxiv.org/abs/2503.09925

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