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arXiv · 2608.08138

EFFEKT: Efficient Federated Knowledge Transfer to Foundation Models

Matteo Caligiuri·Francesco Barbato·Pietro Zanuttigh·Francesco Restuccia
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Abstract

Recent data protection laws have accelerated the adoption of Federated Learning (FL) for privacy-preserving decentralized training. Nevertheless, increasing model sizes impose substantial computational demands on client devices, limiting FL applicability in resource-constrained settings. We introduce a novel multi-domain federated learning framework in which lightweight client-side proxy models collaborate with a server-side Foundation Model (FM) to learn new concepts without sharing private data. Our approach, EFFEKT, enables efficient server-side training of domain-specific LoRA adapters while preserving feature-space alignment between the FM and proxy extractors via novel bi-directional cross-distillation strategies. Experiments on multiple real-world datasets and deployments on low-power edge devices demonstrate improvements over state-of-the-art baselines in most considered domains while maintaining lightweight computation at the client side.

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Matteo Caligiuri, Francesco Barbato, Pietro Zanuttigh, Francesco Restuccia. 2026-08-08. EFFEKT: Efficient Federated Knowledge Transfer to Foundation Models. https://arxiv.org/abs/2608.08138

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