FedM2M: Robust Federated Voiceprint Recognition via Memory-Momentum Meta-Learning

Federated voiceprint recognition enables decentralized biometric authentication with enhanced adaptability to heterogeneous clients. Conventional federated learning (FL) methods often face significant challenges due to data heterogeneity among clients, resulting in suboptimal performance. This paper presents FedM2M (Federated Meta-Learning with Model Memory and Momentum Increment), a novel framework designed to enhance model adaptability and generalization in heterogeneous audio environments. Through the integration of model-agnostic meta-learning for adaptive local training, coupled with gradient increment optimization and momentum-based convergence stabilization, FedM2M effectively addresses the challenges of non-independent and identically distributed (non-IID) data distributions. Extensive empirical evaluation across benchmark datasets, including Librispeech, VoxCeleb, and Zhvoice, demonstrates that FedM2M consistently outperforms state-of-the-art FL algorithms. Ablation analysis further confirms FedM2M's superior accuracy and robustness, particularly in non-IID scenarios, establishing its efficacy as a robust solution for real-world voiceprint recognition applications.