Coordinated Backdoor Attacks against Federated Learning with Model-Dependent Triggers

Federated learning enables distributed training of deep learning models among user equipment (UE) to obtain a high-quality global model. A centralized server aggregates the updates submitted by UEs without knowledge of the local training data or process. Despite its privacy preserving merit, we reveal a severe security concern. Malicious UEs can manipulate their training data by injecting a back-d...