Fault-Tolerant Framework with Federated Learning for Reliable and Robust Distributed System

Gour, Lokendra; Waoo, Akhilesh A.

doi:10.4108/eai.16-4-2022.2318146

Cited by 2 publications

(1 citation statement)

References 0 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Most importantly, the fault-tolerant can be seen as the biggest advantage of FedGroup EL. FedGroup is available to tolerate adversarial attacks and resolve faults since it is deployed on multiple edge devices [32]. Besides, the structure of ensemble learning allows it to take benefits from many models without worrying about causing system failures.…”

Section: Fedgroup Elmentioning

confidence: 99%

Privacy-Aware Anomaly Detection in IoT Environments using FedGroup: A Group-Based Federated Learning Approach

Zhang

Suleiman

Alibasa

et al. 2023

Preprint

View full text Add to dashboard Cite

Concerns on the data security and privacy of smart home users have been growing popularity due to the rising usage of IoT devices. Many traditional machine learning techniques have been used to perform anomaly detections. However, these models need to send private IoT data to a central model for validation and training, raising security and efficiency issues. We propose a new Federated Learning (FL) method called FedGroup, which adopts the FedAvg method, but it updates the learning of the central model based on the learning changes brought by each group of IoT devices. Our experimental results showed that FedGroup achieved same or better anomaly detection accuracy compared to other federated and non-federated learning methods. Furthermore, we showed how ensemble learning may be used to connect many contributing models for superior average prediction performance. FedGroup also improve the detection of attack type detection and attack type detail detection. By comparing our new models with baseline models, our models performed better showing an accuracy of 99.64% accuracy with 0.02% FPR on attack type detection and 99.89% accuracy on attack type detail detection.

show abstract

Section: Fedgroup Elmentioning

confidence: 99%

Privacy-Aware Anomaly Detection in IoT Environments using FedGroup: A Group-Based Federated Learning Approach

Zhang

Suleiman

Alibasa

et al. 2023

Preprint

View full text Add to dashboard Cite

show abstract

Privacy-Aware Anomaly Detection in IoT Environments using FedGroup: A Group-Based Federated Learning Approach

Zhang,

Suleiman,

Alibasa

et al. 2024

J Netw Syst Manage

View full text Add to dashboard Cite

The popularity of Internet of Things (IoT) devices in smart homes has raised significant concerns regarding data security and privacy. Traditional machine learning (ML) methods for anomaly detection often require sharing sensitive IoT data with a central server, posing security and efficiency challenges. In response, this paper introduces FedGroup, a novel Federated Learning (FL) method inspired by FedAvg. FedGroup revolutionizes the central model’s learning process by updating it based on the learning patterns of distinct groups of IoT devices. Our experimental results demonstrate that FedGroup consistently achieves comparable or superior accuracy in anomaly detection when compared to both federated and non-federated learning methods. Additionally, Ensemble Learning (EL) collects intelligence from numerous contributing models, leading to enhanced prediction performance. Furthermore, FedGroup significantly improves the detection of attack types and their details, contributing to a more robust security framework for smart homes. Our approach demonstrates exceptional performance, achieving an accuracy rate of 99.64% with a minimal false positive rate (FPR) of 0.02% in attack type detection, and an impressive 99.89% accuracy in attack type detail detection.

show abstract

Fault-Tolerant Framework with Federated Learning for Reliable and Robust Distributed System

Cited by 2 publications

References 0 publications

Privacy-Aware Anomaly Detection in IoT Environments using FedGroup: A Group-Based Federated Learning Approach

Privacy-Aware Anomaly Detection in IoT Environments using FedGroup: A Group-Based Federated Learning Approach

Privacy-Aware Anomaly Detection in IoT Environments using FedGroup: A Group-Based Federated Learning Approach

Contact Info

Product

Resources

About