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.

翻译：在人工智能（AI）领域中，数据处理对隐私与安全的需求已变得至关重要。随着AI应用的持续扩展，敏感数据的收集与处理引发了对个体隐私保护的担忧。联邦学习（FL）作为一种有望应对这些挑战的解决方案应运而生，它通过在本地设备上进行去中心化模型训练来保护数据隐私。本文介绍FLEX：一个专为FL研究实验提供最大灵活性的联邦学习框架。通过提供可定制的数据分布、隐私参数和通信策略等特性，FLEX使研究人员能够创新并开发新型FL技术。该框架还包含针对特定FL实现的库，涵盖：（1）异常检测、（2）区块链、（3）对抗攻击与防御、（4）自然语言处理以及（5）决策树，从而增强了其在多个领域的通用性与适用性。总体而言，FLEX代表了FL研究的重大进展，促进了鲁棒且高效的FL应用的发展。