Federated Adapter on Foundation Models: An Out-Of-Distribution Approach

• URL: http://arxiv.org/abs/2505.01075v1
• Date: Fri, 02 May 2025 07:33:00 GMT
• Title: Federated Adapter on Foundation Models: An Out-Of-Distribution Approach
• Authors: Yiyuan Yang, Guodong Long, Tianyi Zhou, Qinghua Lu, Shanshan Ye, Jing Jiang,
• Abstract summary: We propose a privacy-preserving approach to fine-tune Federated Foundation Models (FedFM)<n>FedOA employs adapter-based parameter-tuning methods for efficacy and introduces distance-based regularization to distributions and guarantee OOD generalization for each client.
• Score: 42.31209296544899
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: As foundation models gain prominence, Federated Foundation Models (FedFM) have emerged as a privacy-preserving approach to collaboratively fine-tune models in federated learning (FL) frameworks using distributed datasets across clients. A key challenge for FedFM, given the versatile nature of foundation models, is addressing out-of-distribution (OOD) generalization, where unseen tasks or clients may exhibit distribution shifts leading to suboptimal performance. Although numerous studies have explored OOD generalization in conventional FL, these methods are inadequate for FedFM due to the challenges posed by large parameter scales and increased data heterogeneity. To address these, we propose FedOA, which employs adapter-based parameter-efficient fine-tuning methods for efficacy and introduces personalized adapters with feature distance-based regularization to align distributions and guarantee OOD generalization for each client. Theoretically, we demonstrate that the conventional aggregated global model in FedFM inherently retains OOD generalization capabilities, and our proposed method enhances the personalized model's OOD generalization through regularization informed by the global model, with proven convergence under general non-convex settings. Empirically, the effectiveness of the proposed method is validated on benchmark datasets across various NLP tasks.

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