Generalizable Federated Learning using Client Adaptive Focal Modulation

Tajamul Ashraf; Iqra Altaf Gillani

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arXiv · 2508.10840

Generalizable Federated Learning using Client Adaptive Focal Modulation

Tajamul Ashraf·Iqra Altaf Gillani

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Abstract

Federated learning (FL) has proven essential for privacy-preserving, collaborative training across distributed clients. Our prior work, TransFed, introduced a robust transformer-based FL framework that leverages a learn-to-adapt hypernetwork to generate personalized focal modulation layers per client, outperforming traditional methods in non-IID and cross-domain settings. In this extended version, we propose AdaptFED, where we deepen the investigation of focal modulation in generalizable FL by incorporating: (1) a refined adaptation strategy that integrates task-aware client embeddings to personalize modulation dynamics further, (2) enhanced theoretical bounds on adaptation performance, and (3) broader empirical validation across additional modalities, including time-series and multilingual data. We also introduce an efficient variant of TransFed that reduces server-client communication overhead via low-rank hypernetwork conditioning, enabling scalable deployment in resource-constrained environments. Extensive experiments on eight diverse datasets reaffirm the superiority of our method over state-of-the-art baselines, particularly in source-free and cross-task federated setups. Our findings not only extend the capabilities of focal modulation in FL but also pave the way for more adaptive, scalable, and generalizable transformer-based federated systems. The code is available at http://github.com/Tajamul21/TransFed

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Tajamul Ashraf, Iqra Altaf Gillani. 2025-08-14. Generalizable Federated Learning using Client Adaptive Focal Modulation. https://arxiv.org/abs/2508.10840

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