FLADD: Federated Learning-Based Privacy Protection for Audio Deepfake Detection

Current research on Audio Deepfake Detection (ADD) mainly relies on centralized training paradigms, while paying limited attention to privacy preservation under data localization regulations or user-consent constraints. In real-world scenarios, centralized data sharing may expose sensitive user information and hinder the practical deployment of ADD systems. To address this challenge, this paper proposes FLADD, a federated learning-based framework for ADD that supports data-localized training while retaining competitive detection performance. FLADD adopts a dual-branch architecture combining Res2Net's multi-scale spectral feature extraction with Rawformer's temporal modeling of raw waveforms, capturing complementary cues for distinguishing bona fide and spoofed speech. To improve robustness under heterogeneous client data, we introduce a Multi-Factor Adaptive Weighting (MFAW) mechanism, which dynamically adjusts aggregation weights by jointly considering sample size and local model performance, alleviating the bias of conventional size-based aggregation. In addition, FLADD employs a dual-prototype strategy, where global prototypes are constructed separately for the Res2Net and Rawformer branches to guide local optimization under heterogeneous data distributions, enhancing detection capability. Comprehensive experiments on the ASVspoof 2019 LA and ASVspoof 2021 LA evaluation sets demonstrate strong and competitive detection performance, with best observed Equal Error Rates of 0.285% and 2.045%, respectively. FLADD keeps raw audio local and provides empirical analyses of speaker-, content-, and transmitted-channel leakage.