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.
18 articles. Page 2 of 3.
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 readGuide
Designing a Decentralized Federated Learning System
Design a DFL system with topology, message contracts, mixing weights, recovery policies and per-client evaluation.
5 min readResearch note
DFL Is More Than Federated Learning Without a Server
Why DFL changes coordination, consensus and trust. Identify central dependencies and test which services survive a failure.
5 min readGuide
From Survey to Prototype: A Research Workflow for DFL
From the DFL survey to Flighter and Modalis: turn literature into testable hypotheses, reproducible experiments and traceable evidence.
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.