Motion sensors integrated into wearable and mobile devices provide valuable information about the device users. Machine learning and, recently, deep learning techniques have been used to characterize sensor data. Mostly, a single task, such as recognition of activities, is targeted, and the data is processed centrally at a server or in a cloud environment. However, the same sensor data can be utilized for multiple tasks and distributed machine-learning techniques can be used without the requirement of the transmission of data to a centre. This paper explores Federated Transfer Learning in a Multi-Task manner for both sensor-based human activity recognition and device position identification tasks. The OpenHAR framework is used to train the models, which contains ten smaller datasets. The aim is to obtain model(s) applicable for both tasks in different datasets, which may include only some label types. Multiple experiments are carried in the Flower federated learning environment using the DeepConvLSTM architecture. Results are presented for federated and centralized versions under different parameters and restrictions. By utilizing transfer learning and training a task-specific and personalized federated model, we obtained a similar accuracy with training each client individually and higher accuracy than a fully centralized approach.

翻译：集成在可穿戴和移动设备中的运动传感器提供了关于设备用户的宝贵信息。机器学习以及近期的深度学习技术已被用于表征传感器数据。多数情况下，目标在于单一任务（如活动识别），且数据在服务器或云端环境中集中处理。然而，同一传感器数据可用于多个任务，并且可以采用分布式机器学习技术，无需将数据传输至中心。本文以多任务方式探索了联邦迁移学习在基于传感器的人类活动识别和设备位置识别任务中的应用。采用包含十个较小数据集的OpenHAR框架来训练模型，旨在获得适用于不同数据集的模型（这些数据集可能仅包含部分标签类型）。在Flower联邦学习环境中，使用DeepConvLSTM架构进行了多项实验。展示了在不同参数和限制条件下联邦与集中式版本的结果。通过利用迁移学习并训练任务特定且个性化的联邦模型，我们获得了与单独训练每个客户端相似的准确率，且高于完全集中式方法。