FedRDF: A Robust and Dynamic Aggregation Function Against Poisoning Attacks in Federated Learning
Open accessAbstract
Federated Learning (FL) represents a promising approach to typical privacy concerns associated with centralized Machine Learning (ML) deployments. Despite its well-known advantages, FL is vulnerable to security attacks such as Byzantine behaviors and poisoning attacks, which can significantly degrade model performance and hinder convergence. The effectiveness of existing approaches to mitigate complex attacks, such as median, trimmed mean, or Krum aggregation functions, has been only partially demonstrated in the case of specific attacks. Our study introduces a novel robust aggregation mechanism utilizing the Fourier Transform (FT), which is able to effectively handle sophisticated attacks without prior knowledge of the number of attackers. Employing this data technique, weights generated by FL clients are projected into the frequency domain to ascertain their density function, selecting the one exhibiting the highest frequency. Consequently, malicious clients’ weights are excluded. Our proposed approach was tested against various model poisoning attacks, demonstrating superior performance over state-of-the-art aggregation methods.
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Techniques such as Byzantine-resilient aggregation methods and clustering-based anomaly detection can filter out compromised updates and have presented promise.
“Techniques such as Byzantine-resilient aggregation methods and clustering-based anomaly detection, which can filter out compromised updates, have presented promise.”
From Federated Learning in Smart Healthcare: A Comprehensive Review on Privacy, Security, and Predictive Analytics with IoT Integration (2024) · cites this paper as [79]
Robust Federated Averaging evaluates model updates to detect exceptions that diverge significantly from the majority.
“Another method, robust Federated Averaging (FedAvg), evaluates model updates to detect exceptions that diverge significantly from the majority [79].”
From Federated Learning in Smart Healthcare: A Comprehensive Review on Privacy, Security, and Predictive Analytics with IoT Integration (2024) · cites this paper as [79]
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Abbas, S. R., Abbas, Z., Zahir, A., & Lee, S. W. (2024) · Healthcare · doi:10.3390/healthcare12242587 · Cited by 228 · see citing works
- Authors
- Campos, E. M., Gonzalez-Vidal, A., Hernández-Ramos, J. L., & Skarmeta, A.
- Affiliations
- Universidad de Murcia (ES)
- University of Murcia, Murcia, Spain
- Venue
- IEEE Transactions on Emerging Topics in Computing
- Published
- 2025-01
- Volume
- 13
- Issue
- 1
- Pages
- 48-67
- ISSN
- 2168-6750 (ISSN-L)
- 2376-4562
- OpenAlex
- W4403295565
- Cited by
- 22
- References
- 30
- Citations by year
- 2026: 8 · 2025: 8 · 2024: 6
- FWCI
- 3.26
- Type
- journal article
- Language
- en
- Open access
- hybrid
- doi.org
- Licence
- Locations
- IEEE Transactions on Emerging Topics in Computing (publishedVersion, cc-by-nc-nd)
- arXiv (Cornell University) (submittedVersion, cc-by)
- arXiv (Cornell University) (cc-by)
- Funders
- European Commission (PRTRC17, PRTR C17.I1, C17.I1)
- Comunidad Autónoma de la Región de Murcia
- HORIZON EUROPE Marie Sklodowska-Curie Actions (101065524)
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › 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
- Keywords
- federated learning, model poisoning, robust aggregation, Fourier transform, Byzantine attacks, frequency domain analysis, malicious client detection
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