CLAD: A Clustered Label-Agnostic Federated Learning Framework for Joint Anomaly Detection and Attack Classification
Training security systems across IoT devices without sharing raw data
A new framework called CLAD trains security systems across thousands of IoT devices while keeping data private and handling the reality that most collected data comes without labels. It achieves 30% better detection of network attacks than existing methods while using half the communication bandwidth, even when 80% of the data lacks security labels.
As factories, smart homes, and critical infrastructure rely on millions of connected devices, security breaches can cascade rapidly across networks. CLAD makes it practical for these devices to collectively learn threat patterns without exposing sensitive operational data to central servers, while actually improving detection accuracy by making use of unlabeled data that would otherwise be wasted.