Apple ML details a practical recipe for semi-supervised federated ASR with online pseudo-labels
Apple ML Research · rss · 2026-09-24
Apple ML Research published a practical recipe for semi-supervised federated learning (SSFL) in automatic speech recognition.
- Problem: pseudo-label errors in ASR compound across output sequences and training rounds, causing divergence and a large gap to fully-supervised FL.
- Key insight: closing the gap hinges on two coupled design axes — the teacher (which model generates pseudo-labels) and the anchor (server-side updates on a small labeled seed dataset that stabilize training).
- Recipe: online pseudo-labels combined with server update stabilization.
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