FLEX: Flexible Federated Learning Framework Herrera Triguero, Francisco Jiménez López, Daniel Argente-Garrido, Alberto Rodríguez Barroso, Nuria Zuheros, Cristina Aguilera Martos, Ignacio Bello, Beatriz García-Márquez, Mario Luzón García, María Victoria Federated Learning Distributed machine learning Data Privacy Research software framework This research results from the Strategic Project IAFER-Cib (C074/23), as a result of the collaboration agreement signed between the National Institute of Cybersecurity (INCIBE) and the University of Granada. This initiative is carried out within the framework of the Recovery, Transformation and Resilience Plan funds, financed by the European Union (Next Generation). In the realm of Artificial Intelligence (AI), the need for privacy and security in data processing has become paramount. As AI applications continue to expand, the collection and handling of sensitive data raise concerns about individual privacy protection. Federated Learning (FL) emerges as a promising solution to address these challenges by enabling decentralized model training on local devices, thus preserving data privacy. This paper introduces FLEX: a FLEXible Federated Learning Framework designed to provide maximum flexibility in FL research experiments. By offering customizable features for data distribution, privacy parameters, and communication strategies, FLEX empowers researchers to innovate and develop novel FL techniques. The framework also includes libraries for specific FL implementations including: (1) anomalies, (2) blockchain, (3) adversarial attacks and defences, (4) natural language processing and (5) decision trees, enhancing its versatility and applicability in various domains. Overall, FLEX represents a significant advancement in FL research, facilitating the development of robust and efficient FL applications. 2025-02-17T13:11:25Z 2025-02-17T13:11:25Z 2025 journal article Published version: F. Herrera et al. Information Fusion 117 (2025) 102792. https://doi.org/10.1016/j.inffus.2024.102792 https://hdl.handle.net/10481/102415 10.1016/j.inffus.2024.102792 eng http://creativecommons.org/licenses/by-nc-nd/4.0/ open access Attribution-NonCommercial-NoDerivatives 4.0 Internacional Elsevier