Can I read SHFL: Secure Hierarchical Federated Learning Framework for Edge Networks on EtoBox?

SHFL: Secure Hierarchical Federated Learning Framework for Edge Networks by Tavallaie, Omid; Thilakarathna, Kanchana; Seneviratne, Suranga; Seneviratne, Aruna; Zomaya, Albert Y. is a scholarly article available to read on EtoBox.

What is SHFL: Secure Hierarchical Federated Learning Framework for Edge Networks about?

Federated Learning (FL) is a distributed machine learning paradigm designed for privacy-sensitive applications that run on resource-constrained devices with non-Identically and Independently Distributed (IID) data. Traditional FL frameworks adopt the client-server model with a single-level aggregation (AGR) process, where the server builds the global model by aggregating all trained local models received from client devices. However, this conventional approach encounters challenges, including susceptibility to model/data poisoning attacks. In recent years, advancements in the Internet of Things (IoT) and edge computing have enabled the development of hierarchical FL systems with a two-level AGR process running at edge and cloud servers. In this paper, we propose a Secure Hierarchical FL (SHFL) framework to address poisoning attacks in hierarchical edge networks. By aggregating trained models at the edge, SHFL employs two novel methods to address model/data poisoning attacks in the presence of client adversaries: 1) a client selection algorithm running at the edge for choosing IoT devices to participate in training, and 2) a model AGR method designed based on convex optimization the

Author

Tavallaie, Omid; Thilakarathna, Kanchana; Seneviratne, Suranga; Seneviratne, Aruna; Zomaya, Albert Y.

Published

2024

Language

EN