FedFitTech：健身追踪中联邦学习的基线
FedFitTech: A Baseline in Federated Learning for Fitness Tracking
AI总结：
本文提出了基于Flower框架的FedFitTech基线，用于解决健身追踪中联邦学习面临的数据不平衡和个性化等挑战，并通过案例研究证明其早停策略能减少13%冗余通信且仅损失1%识别性能。
摘要：
传感器和资源高效机器学习模型的快速发展，推动了可穿戴健身追踪设备的广泛普及。这些设备配备了惯性传感器，能够连续捕捉身体运动以用于健身技术（FitTech），从而实现从运动优化到预防性医疗保健的应用。传统的集中式学习方法在检测健身活动时，面临数据隐私问题、监管限制和通信效率低下的挑战。相比之下，联邦学习（FL）通过传输模型更新而非潜在私密的可穿戴传感器数据，实现了去中心化的模型训练。将 FL 应用于 FitTech 面临独特挑战，如数据不平衡、缺乏标注数据、用户活动异构，以及个性化与泛化之间的权衡。为简化 FL 中 FitTech 的研究，我们提出了 FedFitTech 基线，它基于 Flower 框架构建，该框架公开可用且被工业界和学术界研究人员广泛使用。此外，为说明其用法，本文提出了一个基于 FedFitTech 基线实现系统的案例研究，该系统结合了客户端早停策略并对结果进行了比较。例如，该系统允许可穿戴设备优化捕捉常见健身活动与保留个体细微差异之间的权衡，从而增强隐私感知健身追踪应用的可扩展性和效率。结果表明，该系统将总体冗余通信减少了 13%，同时以 1% 的可忽略识别成本维持了总体识别性能。因此，FedFitTech 基线为 FitTech 领域广泛的研发新机遇奠定了基础，其开源地址为：https://github.com/shreyaskorde16/FedFitTech
英文摘要：
The rapid evolution of sensors and resource-efficient machine learning models has spurred the widespread adoption of wearable fitness tracking devices. Equipped with inertial sensors, such devices can continuously capture physical movements for fitness technology (FitTech), enabling applications from sports optimization to preventive healthcare. Traditional Centralized Learning approaches to detect fitness activities struggle with data privacy concerns, regulatory restrictions, and communication inefficiencies. In contrast, Federated Learning (FL) enables a decentralized model training by communicating model updates rather than potentially private wearable sensor data. Applying FL to FitTech presents unique challenges, such as data imbalance, lack of labeled data, heterogeneous user activities, and trade-offs between personalization and generalization. To simplify research on FitTech in FL, we present the FedFitTech baseline, under the Flower framework, which is publicly available and widely used by both industry and academic researchers. Additionally, to illustrate its usage, this paper presents a case study that implements a system based on the FedFitTech baseline, incorporating a client-side early stopping strategy and comparing the results. For instance, this system allows wearable devices to optimize the trade-off between capturing common fitness activities and preserving individuals' nuances, thereby enhancing both the scalability and efficiency of privacy-aware fitness tracking applications. The results show that this reduces the overall redundant communications by 13%, while maintaining the overall recognition performance at a negligible recognition cost by 1%. Thus, the FedFitTech baseline creates a foundation for a wide range of new research and development opportunities in FitTech, and it is available as open source at: https://github.com/shreyaskorde16/FedFitTech
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
