Federated Learning for Cybersecurity: Challenges and Future Directions
A special issue of Applied Sciences (ISSN 2076-3417). This special issue belongs to the section "Computing and Artificial Intelligence".
Deadline for manuscript submissions: 20 October 2025 | Viewed by 246
Special Issue Editors
Interests: artificial intelligence; swarm intelligence; edge intelligence; privacy protection; network security
Interests: edge computing and edge intelligence; AI security; security and privacy
Special Issues, Collections and Topics in MDPI journals
Special Issue Information
Dear Colleagues,
Cybersecurity threats are evolving unprecedentedly, necessitating advanced solutions that balance robust threat detection with stringent data privacy requirements. Federated Learning (FL), a decentralized machine learning paradigm, has emerged as a transformative approach to address these dual challenges. By enabling collaborative model training across distributed devices or servers without centralized data aggregation, FL mitigates privacy risks while fostering scalable and inclusive threat intelligence. However, deploying FL in cybersecurity contexts introduces unique challenges, including communication efficiency, robustness against adversarial attacks, and heterogeneity in data distributions. This Special Issue seeks to foster the understanding of FL’s role in cybersecurity, advancing both theoretical foundations and practical implementations of FL for cybersecurity. We are pleased to invite you to contribute to this Special Issue, which aims to consolidate cutting-edge research and multidisciplinary perspectives on FL’s applications, limitations, and opportunities in cybersecurity. In this Special Issue, original research articles and reviews are welcome. Research areas may include (but are not limited to) the following:
- Communication-efficient FL for Cybersecurity;
- Privacy-Preserving FL for Cybersecurity;
- Heterogeneity managementin FL for Cybersecurity;
- Adversarial Attacks and Defenses in FL for Cybersecurity;
- Scalability and Resource Optimization in FL for Cybersecurity;
- Blockchain-Enabled FL for Cybersecurity;
- FL for Anomaly Detection and Threat Intelligence;
- FL for Secure IoT and Edge Computing;
- Explainable and Interpretable FL for Cybersecurity;
- Benchmarking and evaluation metricsfor FL in cybersecurity applications.
Dr. Peng Sun
Prof. Dr. Honglong Chen
Dr. Liantao Wu
Guest Editors
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Keywords
- federated learning
- cybersecurity
- attacks and defenses
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