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Peer-to-peer and semi-decentralized learning systems for security-critical environments where raw data cannot be centralized.
DEFENDIS develops a framework for uniquely identifying IoT devices in a distributed manner while solving security threats through decentralized federated learning.
View ProjectA 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.