---
title: "Pseudo-Labeling"
description: "1 posts about Pseudo-Labeling, summarised, each linking to the original."
---

# Pseudo-Labeling
> 1 posts about Pseudo-Labeling, summarised, each linking to the original.

## Articles

### [A Practical Recipe for Semi-Supervised Federated ASR: Online Pseudo-Labels with Server Update Stabilization](https://yomu.fyi/post/a-practical-recipe-for-semi-supervised-federated-asr-online-pseudo-lab.md)
- Company: [Apple](https://yomu.fyi/company/apple.md)
- Published: Sep 24, 2026
- Type: Problem & solution

Semi-supervised federated learning (SSFL) for ASR uses a teacher to generate pseudo-labels on unlabeled client data while the server trains on a small labeled seed. The paper finds that pseudo-label errors compound across the output sequence and training rounds, causing divergence and a large gap to fully-supervised FL. Two coupled design axes close this gap: the teacher type (per-client online, global broadcast, or transitioning) and the anchor (server-side labeled-data updates that stabilize training). A per-client online teacher diverges on its own but, once stabilized, matches or beats the global teacher in-domain and under domain shift. The server must keep training on labeled data between rounds; otherwise the online teacher drifts, and this interleaving governs convergence more than the seed model does. The proposed guidelines improve over the strongest prior method on 9 of 11 pairs, by 20.8% in-domain and 10.0% cross-domain on average.