Discriminative Feature Learning-Based Federated Lightweight Distillation Against Multiple Attacks

Thanks to the advantages of cloud and edge computing, federated learning (FL)–based speech emotion recognition (SER) tasks can be well-scaled to cloud-edge-terminal ecosystems. It aims to characterize emotions while protecting data privacy. However, catastrophic forgetting caused by data heterogeneity, potential system attacks, and possible privacy leakage and communication overhead from parameter sharing have constrained its breakthrough. Some schemes that attempt to tackle the FL bottleneck do not consider these issues comprehensively. We propose a federated distillation-based multiple defense approach (FedMud), which simultaneously considers how to balance system performance, privacy security, and communication overhead. First, It employs a server-side lightweight generator to learn global view knowledge and guides client-side updates through distillation, further mitigating catastrophic forgetting and improving system performance. In addition, we design a multi-path integrated defense paradigm to counter potential system attacks, with a data perturbation technique based on gradient modification, a dynamically weighted selection method, and a privacy-enhanced strategy by capturing discriminative features. Moreover, to minimize parameter leakage, the parameter-decoupled hierarchical sharing mechanism is utilized, which also significantly reduces the communication overhead. The experimental results show that our approach is effective, with gender predictions down to chance levels while maintaining SER performance enhancements.