Dynamic defense against byzantine poisoning attacks in federated learning

Nuria Rodríguez-Barroso; Eugenio Martínez‐Cámara; M. Victoria Luzón; Francisco Herrera

doi:10.1016/j.future.2022.03.003

ScienceGate Book Chapters

JOURNAL ARTICLE

Dynamic defense against byzantine poisoning attacks in federated learning

Nuria Rodríguez-Barroso Eugenio Martínez‐Cámara M. Victoria Luzón Francisco Herrera

Year: 2022 Journal: Future Generation Computer Systems Vol: 133 Pages: 1-9 Publisher: Elsevier BV

DOI: 10.1016/j.future.2022.03.003

Get Full-Text PDF Get Analytical Report

Keywords:

Computer science Byzantine architecture Computer security Federated learning Byzantine fault tolerance Artificial intelligence Distributed computing Ancient history

Metrics

Cited By

8.62

FWCI (Field Weighted Citation Impact)

Refs

0.97

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

Cryptography and Data Security

Physical Sciences → Computer Science → Artificial Intelligence

Dynamic defense against byzantine poisoning attacks in federated learning

Metrics

Citation History

Topics

Related Documents

Defense against local model poisoning attacks to byzantine-robust federated learning

AdaptiveShield: Dynamic Defense Against Decentralized Federated Learning Poisoning Attacks

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

FedMP: A multi-pronged defense algorithm against Byzantine poisoning attacks in federated learning

Adaptive Defense Mechanisms Against Dynamic Poisoning Attacks in Decentralized Federated Learning