3 min readResearch note
Byzantine-Resilient Aggregation for Decentralized Federated Learning
Median, trimmed mean and trust in DFL: examples, local poisoning assumptions and defense costs with legitimate non-IID clients.
Research Notes on Federated Learning, Cybersecurity and Cyberdefense
Research notes and technical guides on federated learning, distributed AI, cyberdefense, trustworthy systems, and privacy-preserving platforms.
30 articles. Page 5 of 5.
3 min readResearch note
Privacy-Preserving IoT Security with Decentralized Federated Learning
Privacy in IoT security with DFL: protected units, differential privacy budgets, secure aggregation and leakage-aware evaluation.
3 min readResearch note
Federated Energy Anomaly Detection for Critical Infrastructure
Federated energy anomaly detection: temporal splits, false alerts, drift and operational evidence for critical infrastructure.
5 min readGuide
NEBULA: A Platform for Decentralized Federated Learning
NEBULA guide: architecture, official documentation and a reproducible protocol for evaluating DFL topology, privacy, attacks and resources.
7 min readGuide
Federated Learning Without Sharing Raw Data
Learn how federated learning and FedAvg work, what differential privacy and secure aggregation protect, and how to evaluate collaboration.
6 min readGuide
Decentralized Federated Learning: Fundamentals and Applications
DFL foundations: peer aggregation, topology, convergence, privacy and communication. Learn when it fits better than server-based FL.