Making Federated Learning Accessible to Scientists: The AI4EOSC Approach

Abstract Access to computing resources is a critical requirement for researchers in a wide diversity of areas. This has become even more important with the rise of artificial intelligence techniques through the training of machine learning and deep learning models. In this sense, the AI4EOSC project aims to respond to this need by delivering an enhanced set of advanced services and tools for the development of artificial intelligence, machine and deep models, such as federated learning, in the European Open Science Cloud (EOSC). Federated learning is a technology in the field of privacy-preserving machine learning techniques that has revolutionized the current state of the art, evolving from classical centralized approaches to allow training models in a decentralized way, without sharing raw data. In this work, we present the production implementation of a federated learning system based on the Flower framework that allows users, without a technological background, to exploit this technique, performing federated learning training within the AI4EOSC platform. The objective is to be able to train this type of architecture in an intuitive way; for this purpose, a user-friendly dashboard has been implemented, whose development will be reviewed. The frameworks and technologies used for this implementation will be exposed together with an example of use from scratch, in order to demonstrate the use of this functionality of the platform. Finally, two scenarios concerning client availability are analyzed.

Recommended citation: Judith Sáinz-Pardo Díaz, Andrés Heredia Canales, Ignacio Heredia Cachá, Viet Tran, Giang Nguyen, Khadijeh Alibabaei, Marta Obregón Ruiz, Susana Rebolledo Ruiz, and Álvaro López García. 2024. Making Federated Learning Accessible to Scientists: The AI4EOSC Approach. In Proceedings of the 2024 ACM Workshop on Information Hiding and Multimedia Security (IH&MMSec 24). Association for Computing Machinery, New York, NY, USA, 253–264. https://doi.org/10.1145/3658664.3659642
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