Federated Foundation Language Model Post-Training Should Focus on Open-Source Models

Nikita Agrawal; Ruben Mayer

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arXiv · 2505.23593

Federated Foundation Language Model Post-Training Should Focus on Open-Source Models

Nikita Agrawal·Ruben Mayer

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Abstract

Post-training of foundation language models has emerged as a promising research domain in federated learning (FL) with the goal to enable privacy-preserving model improvements and adaptations to user's downstream tasks. Recent advances in this area adopt centralized post-training approaches that build upon black-box foundation language models where there is no access to model weights and architecture details. Although the use of black-box models has been successful in centralized post-training, their blind replication in FL raises several concerns. Our opinion is that using black-box models in FL contradicts the core principles of federation such as data privacy and autonomy. In this paper, we critically analyze the usage of black-box models in federated post-training, and provide a detailed account of various aspects of openness and their implications for FL.

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Nikita Agrawal, Ruben Mayer. 2025-05-29. Federated Foundation Language Model Post-Training Should Focus on Open-Source Models. https://arxiv.org/abs/2505.23593

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