FedRDF: A Robust and Dynamic Aggregation Function Against Poisoning Attacks in Federated Learning

Enrique Mármol Campos; Aurora González-Vidal; José L. Hernández-Ramos; Antonio Skármeta

doi:10.1109/tetc.2024.3474484

ScienceGate Book Chapters

JOURNAL ARTICLE

FedRDF: A Robust and Dynamic Aggregation Function Against Poisoning Attacks in Federated Learning

Enrique Mármol Campos Aurora González-Vidal José L. Hernández-Ramos Antonio Skármeta

Year: 2024 Journal: IEEE Transactions on Emerging Topics in Computing Vol: 13 (1)Pages: 48-67 Publisher: Institute of Electrical and Electronics Engineers

DOI: 10.1109/tetc.2024.3474484

Get Full-Text PDF Get Analytical Report

Keywords:

Computer science Robustness (evolution) Function (biology) Computer security Artificial intelligence Computer network Chemistry

Metrics

Cited By

8.30

FWCI (Field Weighted Citation Impact)

Refs

0.96

Citation Normalized Percentile

Is in top 1%

Is in top 10%

Citation History

Topics

Privacy-Preserving Technologies in Data

Physical Sciences → Computer Science → Artificial Intelligence

Adversarial Robustness in Machine Learning

Physical Sciences → Computer Science → Artificial Intelligence

FedRDF: A Robust and Dynamic Aggregation Function Against Poisoning Attacks in Federated Learning

Metrics

Citation History

Topics

Related Documents

PnA: Robust Aggregation Against Poisoning Attacks to Federated Learning for Edge Intelligence

Robust Aggregation Technique Against Poisoning Attacks in Multi-Stage Federated Learning Applications

RFLPA: A Robust Federated Learning Framework against Poisoning Attacks with Secure Aggregation

FLRAM: Robust Aggregation Technique for Defense against Byzantine Poisoning Attacks in Federated Learning

FLCom: Robust federated learning against strong model poisoning attacks