Breaking Data Efficiency Dilemma: A Federated and Augmented Learning Framework For Alzheimer's Disease Detection via Speech

• URL: http://arxiv.org/abs/2602.14655v1
• Date: Mon, 16 Feb 2026 11:26:06 GMT
• Title: Breaking Data Efficiency Dilemma: A Federated and Augmented Learning Framework For Alzheimer's Disease Detection via Speech
• Authors: Xiao Wei, Bin Wen, Yuqin Lin, Kai Li, Mingyang gu, Xiaobao Wang, Longbiao Wang, Jianwu Dang,
• Abstract summary: Early diagnosis of Alzheimer's Disease (AD) is crucial for delaying its progression.<n>While AI-based speech detection is non-invasive and cost-effective, it faces a critical data efficiency dilemma due to medical data scarcity and privacy barriers.<n>We propose FAL-AD, a novel framework that integrates federated learning with data augmentation to systematically optimize data efficiency.
• Score: 45.03975417998131
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: Early diagnosis of Alzheimer's Disease (AD) is crucial for delaying its progression. While AI-based speech detection is non-invasive and cost-effective, it faces a critical data efficiency dilemma due to medical data scarcity and privacy barriers. Therefore, we propose FAL-AD, a novel framework that synergistically integrates federated learning with data augmentation to systematically optimize data efficiency. Our approach delivers three key breakthroughs: First, absolute efficiency improvement through voice conversion-based augmentation, which generates diverse pathological speech samples via cross-category voice-content recombination. Second, collaborative efficiency breakthrough via an adaptive federated learning paradigm, maximizing cross-institutional benefits under privacy constraints. Finally, representational efficiency optimization by an attentive cross-modal fusion model, which achieves fine-grained word-level alignment and acoustic-textual interaction. Evaluated on ADReSSo, FAL-AD achieves a state-of-the-art multi-modal accuracy of 91.52%, outperforming all centralized baselines and demonstrating a practical solution to the data efficiency dilemma. Our source code is publicly available at https://github.com/smileix/fal-ad.

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