Overview

Federated learning in audio processing introduces several unique challenges that require careful consideration. First, data heterogeneity across devices can lead to inconsistencies in audio quality, content, and timing, affecting the reliability of models trained in such decentralized environments. Temporal misalignment, such as variations in sampling rates or delays in audio recordings across devices, further complicates model training by disrupting the synchronization of sequential audio data critical for tasks like speech recognition or event detection. Additionally, the constraints of varying computational resources and storage capacities on different devices exacerbate these issues, making it difficult to ensure model efficacy.

Maintaining privacy while managing large-scale audio data distributed across numerous devices also raises significant security concerns. Robust encryption methods are essential to protect sensitive data during transmission. The asynchronous nature of device connectivity, coupled with potential clock drift or network latency, can result in delays and discrepancies in model updates, undermining the learning algorithm’s performance. Addressing these challenges necessitates the development of algorithms that can accommodate both data diversity and device variability, enhanced security protocols for sensitive audio data, and efficient strategies to manage connectivity, synchronization, and temporal alignment across devices.

In this workshop at IEEE ICASSP 2025, we aim to facilitate the sharing of cutting-edge research, foster collaborations among scholars, and drive forward the practical applications of federated learning in audio processing.