Deploying machine learning (ML) algorithms on mobile phones is bottlenecked by performance degradation under dynamic, real-world conditions that differ from the offline training conditions. While continual learning and adaptation are essential to mitigate this distributional shift, conventional online learning methods are often computationally prohibitive for resource-constrained devices. In this paper, we propose LightTune, a lightweight, backpropagation-free online fine-tuning framework with provable convergence guarantees. LightTune opportunistically refines ML models using live test-time data only when performance falls below a predefined threshold, ensuring minimal computational overhead and highly efficient responsiveness. As a practical demonstration, we integrate LightTune into a block error rate (BLER) prediction algorithm for 6G mobile systems. This integration enables the ML BLER prediction model to dynamically adapt to previously unseen channel conditions in real-time. Our extensive results show a substantial reduction in the average BLER prediction error of up to 48.8% with online fine-tuning. Furthermore, we leverage this BLER prediction algorithm for link adaptation and demonstrate average throughput improvements of up to 15.5% compared to a conventional table-based outer loop link adaptation (OLLA) algorithm.

翻译：在移动设备上部署机器学习算法时，动态且与离线训练条件不同的真实场景会导致性能下降，这已成为主要瓶颈。尽管持续学习与自适应对于缓解这种分布偏移至关重要，但传统在线学习方法对资源受限设备而言计算开销过大。本文提出LightTune——一种具有可证明收敛保证的轻量级无反向传播在线微调框架。LightTune仅在模型性能低于预设阈值时，利用实时测试数据对模型进行机会性优化，从而确保极低计算开销与高效响应能力。作为实践验证，我们将LightTune集成到面向6G移动系统的误块率预测算法中。该集成使得基于机器学习的误块率预测模型能实时适应先前未知的信道条件。大量实验结果表明，通过在线微调，平均误块率预测误差最大降低48.8%。此外，我们利用该误块率预测算法实现链路自适应，相比传统基于查表的外环链路自适应算法，平均吞吐量提升最高达15.5%。