Network Intrusion Detection Based on Convolutional Recurrent Neural Network, Random Forest, and Federated Learning

Autor: Qianying Zou, Yushi Li, Xinyue Jiang, Yuepeng Zan, Fengyu Liu

Publikováno v: Journal of Computing and Information Technology, Vol 32, Iss 2, Pp 97-125 (2024)

This paper presents a novel network intrusion detection framework that combines convolutional recurrent neural networks (CRNN) and random forest (RF) models within a federated learning setting. The proposed approach aims to address the challenges of

Externí odkaz: https://doaj.org/article/e015577f0b9c4c6fadf700804313c0de

Federated Learning Framework for IID and Non-IID datasets of Medical Images

Autor: Kavitha Srinivasan, Sainath Prasanna, Rohit Midha, Shraddhaa Mohan

Publikováno v: Emitter: International Journal of Engineering Technology, Vol 11, Iss 1 (2023)

Advances have been made in the field of Machine Learning showing that it is an effective tool that can be used for solving real world problems. This success is hugely attributed to the availability of accessible data which is not the case for many fi

Externí odkaz: https://doaj.org/article/aed0921820f84ffdb1c643294b289a2f

Federated learning framework for mobile edge computing networks

Autor: Romano Fantacci, Benedetta Picano

Publikováno v: CAAI Transactions on Intelligence Technology (2019)

The continuous growth of smart devices needing processing has led to moving storage and computation from cloud to the network edges, giving rise to the edge computing paradigm. Owing to the limited capacity of edge computing nodes, the presence of po

Externí odkaz: https://doaj.org/article/1615ad3b95124a4f8ec474b2b9e41293

Towards a Federated Learning Framework on a Multi-Cloud Environment

Autor: Brum, Rafaela, Sens, Pierre, Arantes, Luciana, Castro, Maria, Drummond, Lucia

Publikováno v: IEEE SBAC-PADW 2022-IEEE 34th International Symposium on Computer Architecture and High Performance Computing Workshops
IEEE SBAC-PADW 2022-IEEE 34th International Symposium on Computer Architecture and High Performance Computing Workshops, Nov 2022, Bordeaux, France. pp.39-44, ⟨10.1109/SBAC-PADW56527.2022.00016⟩

International audience; This paper proposes Multi-FedLS, a Cross-silo Federated Learning (FL) framework for a multi-cloud environment aiming at minimizing financial cost as well as execution time. It comprises four modules: Pre-Scheduling, Initial Ma

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::87dcfbec2943733122e235b2e622eaa9
https://doi.org/10.1109/sbac-padw56527.2022.00016

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