FedMLAC通过互学习机制同时解决音频联邦学习中的三类难题。
FedMLAC: Mutual Learning Driven Heterogeneous Federated Audio Classification
- 客户端本地模型与共享插件模型双向知识蒸馏，适应异构数据和模型。
- 在四个音视频基准上平均提升1.8%准确率，对噪声数据鲁棒性更强。
- 适合语音识别、环境声音分类等需隐私保护的场景使用。
联邦学习（FL）为在去中心化客户端间训练音频分类（AC）模型提供了一种保护隐私的框架，无需共享原始数据。然而，联邦音频分类（FedAC）面临三大挑战：数据异构性、模型异构性以及数据投毒，这些因素在真实场景中会降低性能。现有方法通常单独处理这些问题，统一且稳健的解决方案仍待探索。本文提出FedMLAC，一种基于互学习的联邦学习框架，可同时应对上述三类挑战。每个客户端维护一个个性化本地音频分类模型及一个轻量级全局共享的插件模型。这两个模型通过双向知识蒸馏交互，实现全局知识共享的同时适配本地数据分布，从而缓解数据与模型异构性。为对抗数据投毒，引入分层剪枝聚合（LPA）策略，在聚合过程中基于参数偏差过滤异常的插件更新。在四个多样化的音频分类基准上的大量实验表明，包括语音与非语音任务，FedMLAC在分类准确率和对噪声数据的鲁棒性方面均持续优于现有最先进基线。
原文摘要 · Abstract (English)
Federated Learning (FL) offers a privacy-preserving framework for training audio classification (AC) models across decentralized clients without sharing raw data. However, Federated Audio Classification (FedAC) faces three major challenges: data heterogeneity, model heterogeneity, and data poisoning, which degrade performance in real-world settings. While existing methods often address these issues separately, a unified and robust solution remains underexplored. We propose FedMLAC, a mutual learning-based FL framework that tackles all three challenges simultaneously. Each client maintains a personalized local AC model and a lightweight, globally shared Plug-in model. These models interact via bidirectional knowledge distillation, enabling global knowledge sharing while adapting to local data distributions, thus addressing both data and model heterogeneity. To counter data poisoning, we introduce a Layer-wise Pruning Aggregation (LPA) strategy that filters anomalous Plug-in updates based on parameter deviations during aggregation. Extensive experiments on four diverse audio classification benchmarks, including both speech and non-speech tasks, show that FedMLAC consistently outperforms state-of-the-art baselines in classification accuracy and robustness to noisy data.
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