Title: Real-time cross-lingual speech translation for English teaching via federated transfer learning and multi-modal knowledge distillation

Authors: Baoying Sun; Yingwei Liu; Ying Huang

Addresses: Jilin Agricultural Science and Technology College, Jilin, China ' Shenzhen Jihua Weite Electronics Co., Ltd., Shenzhen, China ' Jilin Agricultural Science and Technology College, Jilin, China

Abstract: This paper presents a novel approach for real-time cross-lingual speech translation using federated transfer learning and multi-modal knowledge distillation. Real-time translation between languages is critical for many applications in today's globalised world, but existing solutions often face significant challenges such as high latency, computational overhead, and data privacy concerns. In this work, we address these challenges by introducing a decentralised learning framework using federated transfer learning to enable privacy-preserving model training across edge devices, and multi-modal knowledge distillation to transfer knowledge from a large, accurate teacher model to smaller, more efficient student models. Our approach not only reduces the latency and computational cost of speech translation systems but also ensures high translation quality. Experimental results demonstrate that our model outperforms traditional methods in terms of translation accuracy, latency, and model size, making it well-suited for deployment on edge devices with limited resources.

Keywords: real-time cross-lingual speech translation; federated transfer learning; FTL; multi-modal knowledge distillation; edge computing; privacy-preserving machine learning.

DOI: 10.1504/IJBIDM.2026.155250

International Journal of Business Intelligence and Data Mining, 2026 Vol.28 No.4/5/6, pp.362 - 380

Received: 17 Aug 2025
Accepted: 06 Jan 2026
Published online: 29 Jul 2026 *

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