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Research notes and technical guides on federated learning, distributed AI, cyberdefense, trustworthy systems, and privacy-preserving platforms.
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A practical note on how distributed monitoring, DFL models, alert evidence and LLM-based support can fit into a cyberdefense workflow.
A research note on using DFL to turn distributed telemetry, anomalies and trust signals into cyberdefense situational awareness.
A technical note on how drone fleets can use DFL to collaborate on detection models without exposing mission telemetry.
A focused research note on median, trimmed mean and trust-weighted aggregation for peer-to-peer federations under poisoning and unreliable clients.
A research note on edge training, secure aggregation and adaptive privacy budgets for IoT security monitoring.
A research note on decentralized anomaly detection where privacy budgets adapt to threat level, criticality and model drift.
This guide reviews NEBULA as a platform for decentralized federated learning, from basic concepts to applications in healthcare, IoT, and cybersecurity, with practical implementation examples.
An introduction to federated learning as a way to train models collaboratively without sharing raw data.
An introduction to decentralized federated learning, from mathematical foundations to applications in cybersecurity and other domains.