A federated transformer-enhanced double Q-network for collaborative intrusion detection

With the rapid proliferation of IoT technology, cybersecurity challenges have become increasingly prominent. Traditional centralized network intrusion detection systems (NIDS) exhibit significant limitations including privacy risks and inadequate modeling of spatiotemporal correlations. This paper proposes FedT-DQN (Transformer-based Federated Double Q-Network), a novel federated reinforcement learning framework for dynamic intrusion detection. The method incorporates: (1) A Transformer encoder as federated aggregator using self-attention mechanisms; (2) A dual-layer Q-network architecture decomposing detection into feature extraction and decision optimization; (3) Soft Actor-Critic (SAC) integration for local training considering system heterogeneity. Experimental results on four benchmark datasets show that FedT-DQN achieves over [Formula: see text] detection accuracy with enhanced F1 scores and reduced false positive rates, all while maintaining data privacy. The source code for this study is available at https://github.com/BuLaTaa/FedT-DQN-in-IDS. .