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SpeechLLM Meets Federated Learning for End-to-End ASR: English and Italian Case Studies

Research Multimodal & Generative

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TL;DR - This paper presents a communication-efficient federated learning strategy for end-to-end SpeechLLM-based speech recognition. English and Italian case studies show competitive accuracy and stable decentralized training while reducing communication costs.

  • Addresses high-dimensional parameters, gradient overhead, and distributed compute constraints.
  • Compares federated and centralized training across varied acoustic conditions and speaking styles.
  • Evaluates how speech encoder architectures affect federated English ASR performance.
  • Establishes a foundation for privacy-preserving, multilingual SpeechLLM deployment.

Sources (1)

SpeechLLM Meets Federated Learning for End-to-End ASR: English and Italian Case Studies

arXiv cs.CL Mohamed Nabih Ali, Daniele Falavigna, Alessio Brutti 2026-07-28 arXiv:2607.25716
Public signals Semantic Scholar citations 0 · Semantic Scholar influential citations 0
Providers: Hugging Face · N/A OpenAlex · N/A Publisher · N/A Semantic Scholar · Citations 0 · Influential citations 0 X · N/A Fetched 2026-08-20 14:30:58.533640 UTC

TL;DR - This paper presents a communication-efficient federated learning strategy for end-to-end SpeechLLM-based speech recognition. English and Italian case studies show competitive accuracy and stable decentralized training while reducing communication costs.

  • Addresses high-dimensional parameters, gradient overhead, and distributed compute constraints.
  • Compares federated and centralized training across varied acoustic conditions and speaking styles.
  • Evaluates how speech encoder architectures affect federated English ASR performance.
  • Establishes a foundation for privacy-preserving, multilingual SpeechLLM deployment.
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