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Robust, explainable and privacy-aware machine learning systems for distributed and adversarial security settings.
DEFENDIS develops a framework for uniquely identifying IoT devices in a distributed manner while solving security threats through decentralized federated learning.
View ProjectROBUST-6G studies security mechanisms for 6G systems, including monitoring, secure data management, trustworthy AI services, federated learning, and threat response.
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 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.