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：在移动手机上部署机器学习算法面临性能瓶颈，原因在于动态、真实运行环境与离线训练条件存在差异，导致性能下降。虽然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往往计算开销过高。本文提出LightTune，一种轻量级、免反向传播的在线微调框架，并具有可证明的收敛保证。LightTune仅在实时推理数据性能低于预设阈值时，方可对机器学习模型进行优化精调，从而确保极小的计算开销和高效响应。作为实际应用验证，我们将LightTune集成至6G移动系统的误块率（BLER）预测算法中。该集成使得机器学习BLER预测模型能够实时动态适应此前未见的信道条件。大量实验结果表明，通过在线微调，平均BLER预测误差最多可降低48.8%。此外，我们利用该BLER预测算法进行链路自适应，相比传统基于查表的外环链路自适应（OLLA）算法，平均吞吐量提升最高达15.5%。