FedOpenHAR: Federated Multi-Task Transfer Learning for Sensor-Based Human Activity Recognition

• URL: http://arxiv.org/abs/2311.07765v1
• Date: Mon, 13 Nov 2023 21:31:07 GMT
• Title: FedOpenHAR: Federated Multi-Task Transfer Learning for Sensor-Based Human Activity Recognition
• Authors: Egemen \.I\c{s}g\"uder and \"Ozlem Durmaz \.Incel
• Abstract summary: 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. 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.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

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