FLEX: FLEXible Federated Learning Framework

Francisco Herrera, Daniel Jiménez-López, Alberto Argente-Garrido, Nuria Rodríguez-Barroso, Cristina Zuheros +4 more
4/9/2024
cs.CRcs.AI

No official code found. Best implementation: SMILELab-FL/FedLab (828 stars, Apache-2.0, PyTorch, last commit 2025-10-20). 1 more below.

Abstract

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.

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Code Implementations(2)

SMILELab-FL/FedLabCommunity100%
PyTorchApache-2.0

A flexible Federated Learning Framework based on PyTorch, simplifying your Federated Learning research.

828141Shell, PythonMar 10, 20211 years agoApache-2.0
deep-learningfederated-learningfederated-learning-frameworkfedlabmachine-learning+2 more
FLEXible-FL/flex-treesCommunity100%
AGPL-3.0

Decision tree methods in federated learning with FLEXible.

33Python, Jupyter NotebookJun 27, 20231 years agoAGPL-3.0
datasetdecision-treefederated-learningflexiblemodels

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Cite this paper

@article{herrera2024flex,
  title  = {FLEX: FLEXible Federated Learning Framework},
  author = {Francisco Herrera and Daniel Jiménez-López and Alberto Argente-Garrido and Nuria Rodríguez-Barroso and Cristina Zuheros and Ignacio Aguilera-Martos and Beatriz Bello and Mario García-Márquez and M. Victoria Luzón},
  year   = {2024},
  eprint = {2404.06127},
  archivePrefix = {arXiv},
  url    = {http://arxiv.org/abs/2404.06127v1}
}

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