# Leveraging Foundation Models for Efficient Federated Learning in Resource-restricted Edge Networks Source: [Leveraging Foundation Models for Efficient Federated Learning in Resource-restricted Edge Networks]() Collection index: [llms.txt]() Data updated: 2025-10-26T03:18:28.201Z Authors: S. Kawa Atapour, S. Jamal SeyedMohammadi, S. Mohammad Sheikholeslami, Jamshid Abouei, Konstantinos N. Plataniotis, Arash Mohammadi Published: 2024-09-14T02:54:31.000Z ## Abstract Recently pre-trained Foundation Models (FMs) have been combined with Federated Learning (FL) to improve training of downstream tasks while preserving privacy. However, deploying FMs over edge networks with resource-constrained Internet of Things (IoT) devices is under-explored. This paper proposes a novel framework, namely, Federated Distilling knowledge to Prompt (FedD2P), for leveraging the robust representation abilities of a vision-language FM without deploying it locally on edge devices. This framework distills the aggregated knowledge of IoT devices to a prompt generator to efficiently adapt the frozen FM for downstream tasks. To eliminate the dependency on a public dataset, our framework leverages perclass local knowledge from IoT devices and linguistic descriptions of classes to train the prompt generator. Our experiments on diverse image classification datasets CIFAR, OxfordPets, SVHN, EuroSAT, and DTD show that FedD2P outperforms the baselines in terms of model performance. ## Recorded benchmark results A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage. ## Original sources and implementation [Original paper]() [PDF]()