.png){kind=icon}
.png){kind=icon}
Return
FLEX: Flexible Federated Learning Framework
.png){kind=icon}
OA
AI
DOI：10.1016/j.inffus.2024.102792.png)
.png){kind=icon}
.png){kind=icon}
Share.png){kind=icon}
Save
Abstract
En 中文
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 and the possibility to deploy federated solutions. By offering customizable features for data distribution, privacy parameters, and communication strategies, FLEX empowers researchers to innovate and develop novel FL techniques. It also provides a distributed version that allows experiments to be deployed on different devices. The framework also includes libraries for specific FL implementations including: (1) anomalies, (2) blockchain, (3) adversarial attacks and defenses, (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 and deployment, facilitating the development of robust and efficient FL applications.
Keywords:
Federated learning
Distributed machine learning
Data privacy
Research software framework
Deployment software framework
AI Summary
Key information extracted from the uploaded paper, including a brief overview, abstract, background, key highlights, visual analysis, and future outlook.