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Federated Foundation Language Model Post-Training Should ...
[Submitted on 29 May 2025 (v1), last revised 14 Jun 2026 (this v · 2026-06-16 · via cs.LG updates on arXiv.org

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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.

Submission history

From: Nikita Agrawal [view email]
[v1] Thu, 29 May 2025 16:04:39 UTC (27 KB)
[v2] Fri, 30 May 2025 13:38:36 UTC (27 KB)
[v3] Tue, 23 Dec 2025 09:55:01 UTC (507 KB)
[v4] Sun, 14 Jun 2026 18:49:15 UTC (483 KB)