Semi-Supervised Federated ASR Training

This post details advancements in Semi-Supervised Federated Learning (SSFL) for Automatic Speech Recognition (ASR). It introduces a practical recipe involving online pseudo-labeling per client and server-side update stabilization. Key findings include the effectiveness of per-client online teachers over broadcast global teachers, the critical role of server-side training on labeled data for convergence, and the sensitivity of stabilization to data augmentation and batch size. The work provides guidelines for improving SSFL in ASR, significantly narrowing the gap to fully-supervised FL.