FedMLAC: Mutual Learning Driven Heterogeneous Federated Audio Classification
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arXiv
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| Main Authors: | , , , , , , , |
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| Format: | Preprint |
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2025
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| _version_ | 1866918111243927552 |
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| author | Bai, Jun Rana, Rajib Wu, Di Qu, Youyang Tao, Xiaohui Zhang, Ji Busso, Carlos Palaiahnakote, Shivakumara |
| author_facet | Bai, Jun Rana, Rajib Wu, Di Qu, Youyang Tao, Xiaohui Zhang, Ji Busso, Carlos Palaiahnakote, Shivakumara |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2506_10207 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | FedMLAC: Mutual Learning Driven Heterogeneous Federated Audio Classification Bai, Jun Rana, Rajib Wu, Di Qu, Youyang Tao, Xiaohui Zhang, Ji Busso, Carlos Palaiahnakote, Shivakumara Sound Distributed, Parallel, and Cluster Computing Audio and Speech Processing 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. |
| title | FedMLAC: Mutual Learning Driven Heterogeneous Federated Audio Classification |
| topic | Sound Distributed, Parallel, and Cluster Computing Audio and Speech Processing |
| url | https://arxiv.org/abs/2506.10207 |