Fed-Adapt: A Federated Learning Framework for Adaptive Topology Reconfiguration Against Multi-Rate DDoS and Database Flooding Attacks

Fed-Adapt is a federated learning framework (a system where multiple computers learn together while keeping their data private) designed to defend networks against DDoS attacks (floods of traffic meant to overwhelm servers) and database flooding attacks (requests that exhaust database resources). The framework addresses the challenge of detecting and responding to these sophisticated attacks in real-time while protecting data privacy across distributed networks, which existing federated learning approaches struggle to do effectively.