# Apple — A Practical Recipe for Semi-Supervised Federated ASR: Online Pseudo-Labels with Server Update Stabilization

- Company: Apple (apple.com)
- Announced: 2026-09-24
- Category: research-paper
- Coverage: not counted
- Announcement: yes
- Group: announcements
- Source: https://machinelearning.apple.com/research/practical-recipe-federated-asr
- Record: https://forck.live/items/13751-a-practical-recipe-for-semi-supervised-federated-asr-online-pseudo-labels-with
- Subject: Machine Learning Research

Apple researchers present a method for semi-supervised federated learning in automatic speech recognition that uses per-client online pseudo-label generation stabilized by server-side updates on labeled data. The approach improves over prior methods on 9 of 11 evaluation pairs, narrowing the gap to fully-supervised federated learning.

## Evidence

Verbatim from https://machinelearning.apple.com/research/practical-recipe-federated-asr:

> These findings yield guidelines for SSFL in ASR training, improving over the strongest prior method on 9 of 11 pairs, by 20.8% on average in-domain and 10.0% cross-domain, narrowing the gap to fully-supervised FL.

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Record: https://forck.live/items/13751-a-practical-recipe-for-semi-supervised-federated-asr-online-pseudo-labels-with
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