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1 Annals of Computer Science and Information Systems

1 2023

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One-shot federated learning with self-adversarial data

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Danilenka A. , Bogacka K. , Wasielewska-Michniewska K.

tom Vol. 35

919--924

Federated learning (FL) is a decentralized approach that aims at training a global model with the help of multiple devices, without collecting or revealing individual clients' data. The training of a federated model is conducted in communication rounds. Still, in certain scenarios, numerous communication rounds are impossible to perform. In such cases, a one-shot FL is utilized, where the number of communication rounds is limited to one. In this article, the idea of one-shot FL is enhanced with the usage of adversarial data, exploring and illustrating the possibilities to improve the performance of resulting global models, including scenarios with non-IID data, for image classification datasets: MNIST and CIFAR-10.