5 min readResearch note
How to Measure Robustness in Adversarial DFL Experiments
Evaluate DFL robustness with F1, false rejection, overhead and recovery. Interpret Flighter and compare clean and attacked conditions.
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.
Topic: Decentralized Federated Learning Clear topic
7 articles. Page 1 of 2.
5 min readResearch note
Flighter: Situational Awareness as a Reliability Signal in DFL
How Flighter combines context and models to evaluate DFL reliability: study scope, indicator ablations and participant recovery.
5 min readResearch note
From Monitoring to Mitigation: A DFL Cyberdefense Lifecycle with LLM Explanations
Connect telemetry, federated learning, alerts and LLM-assisted mitigation with traceable evidence, validation and reviewed feedback.
3 min readResearch note
Situational Awareness for Cyberdefense with Decentralized Federated Learning
Build cyberdefense situational awareness with DFL: provenance, coverage, uncertainty and shared context for operational decisions.
3 min readResearch note
Drones, Edge Intelligence and DFL for Cyberdefense Operations
DFL for drones and edge systems: local inference, intermittent links, node recovery and evaluation of resources and reliability.
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.