Fast and Effective Backdoor Removal in Federated Spiking Neural Networks via Temporal Synaptic Sanitization

Federated learning enables privacy-preserving training of spiking neural networks on distributed neuromorphic and event-based data, but it also exposes the global model to stealthy backdoor attacks injected by malicious clients. Compared with conventional artificial neural networks, federated spiking neural networks are more difficult to sanitize because malicious behavior may be encoded not only in spatial filters but also in spike timing, membrane dynamics, and temporal firing sparsity. Existing backdoor defenses usually require repeated federated retraining, access to client data, trigger synthesis, or computationally expensive model repair, which limits their practicality in low-power neuromorphic deployment. This paper proposes FedTSR, a fast post-training backdoor removal framework for spiking neural networks trained through federated learning. FedTSR introduces two coordinated algorithms: temporal synaptic risk estimation, which identifies backdoor-sensitive synaptic groups by measuring abnormal spike-response contributions across simulation timesteps using a small clean calibration set, and spike-consistency recalibration, which restores benign task performance through lightweight membrane-potential alignment and firing-rate regularization without restarting federated training. The proposed method is trigger-agnostic, client-independent, and compatible with surrogate-gradient trained spiking models. Here, trigger-agnostic means that FedTSR does not use trigger identity during repair; it should not be interpreted as certified robustness against every adaptive trigger family. Experiments on real neuromorphic and vision benchmarks, including N-MNIST, CIFAR10-DVS, DVS Gesture, and CIFAR-10, show that FedTSR substantially reduces attack success rate while preserving clean accuracy under multiple backdoor patterns, poisoning ratios, and non-IID federated settings. The results indicate that exploiting temporal spike dynamics provides an efficient and effective route for sanitizing compromised federated spiking neural networks.