FedNDA: Enhancing Federated Learning with Noisy Client Detection and Robust Aggregation

FedNDA: Enhancing Federated Learning with Noisy Client Detection and Robust Aggregation

Federated Learning is a novel decentralized methodology that enables multiple clients to collaboratively train a global model while preserving the privacy of their local data. Although federated learning enhances data privacy, it faces challenges related to data quality and client behavior. A funda...

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Bibliographic Details
Main Authors: Tuan Dung Kieu, Charles Fonbonne, Trung-Kien Tran, Thi-Lan Le, Hai Vu, Huu-Thanh Nguyen, Thanh-Hai Tran
Format: Article
Language:English
Published: European Alliance for Innovation (EAI) 2025-07-01
Series:EAI Endorsed Transactions on Industrial Networks and Intelligent Systems
Subjects:
Federated learning
Deep learning
Noisy clients
Non-IID
Class imbalance
Online Access:https://publications.eai.eu/index.php/inis/article/view/8720
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