FedHSP: A Robust Federated Learning Framework Coherently Addressing Heterogeneity, Security, and Performance Challenges

FedHSP: A Robust Federated Learning Framework Coherently Addressing Heterogeneity, Security, and Performance Challenges

Federated Learning (FL) is a machine learning training method that leverages local model gradients instead of accessing private data from individual clients, ensuring privacy. However, the practical implementation of FL faces significant challenges. Heterogeneous clients and edge devices with varyin...

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Bibliographic Details
Main Authors: Divya G. Nair, C. V. Aswartha Narayana, K. Jaideep Reddy, Jyothisha J. Nair
Format: Article
Language:English
Published: IEEE 2025-01-01
Series:IEEE Access
Subjects:
Federated learning
sign-flipping attack
additive-noise attack
anomaly detection
device heterogeneity
Online Access:https://ieeexplore.ieee.org/document/10975059/
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