EFFEKT: Efficient Federated Knowledge Transfer to Foundation Models

• URL: http://arxiv.org/abs/2608.08138v1
• Date: Sat, 08 Aug 2026 13:54:48 GMT
• Title: EFFEKT: Efficient Federated Knowledge Transfer to Foundation Models
• Abstract summary: Recent data protection laws have accelerated the adoption of Federated Learning (FL) for privacy-preserving decentralized training.<n>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.<n>Our approach, EFFEKT, enables efficient server-side training of domain-specific LoRA adapters while preserving feature-space alignment between the FM and proxy extractors.
• Score: 16.207029453895604
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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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