Abstract
Human Activity Recognition (HAR) enables computer systems to assist users with their tasks and improve their quality of life in rehabilitation, daily life tracking, fitness, and cognitive disorder therapy. It is a hot topic in the field of machine learning, and HAR is gaining more attention among researchers due to its unique societal and economic advantages. This paper focuses on a collaborative computation scenario where a group of participants will securely and collaboratively train an accurate HAR model. The training process requires collecting a massive number of personal activity features and labels, which raises privacy problems. We decentralize the training process locally to each client in order to ensure the privacy of training data. Furthermore, we use an advanced secure aggregation algorithm to ensure that malicious participants cannot extract private information from the updated parameters even during the aggregation phase. Edge computing nodes have been introduced into our system to address the problem of data generation devices’ insufficient computing power. We replace the traditional central server with smart contract to make the system more robust and secure. We achieve the verifiability of the packaged nodes using the publicly auditability feature of blockchain. According to the experimental data, the accuracy of the HAR model trained by our proposed framework reaches 93.24%, which meets the applicability requirements. The use of secure multiparty computation techniques unavoidably increases training time, and experimental results show that a round of iterations takes 36.4 seconds to execute, which is still acceptable.
Highlights
Human Activity Recognition (HAR) is a machine learning task to identify human activities through images, videos, or sensor data generated by smart wearable devices
Khelifi et al explored the applicability of deep learning models with IoT devices. e study sought to assess the future trends of deep learning plus edge computing in the future. e study points out that convolutional neural models can be used in the IoT domain and that reliable machine learning models can be trained even with data from complex environments [9]
We used a secure aggregation algorithm to ensure that personal information does not leak even throughout the aggregation process
Summary
Human Activity Recognition (HAR) is a machine learning task to identify human activities through images, videos, or sensor data generated by smart wearable devices. Federated learning enables us to keep model training procedure on local devices without transmitting data to central. Used methods, including secure multiparty computation and differential privacy, aim to resist privacy disclosure in the learning process[4, 6] These approaches are often accompanied by a loss of model efficiency or an increase in training time. Edge computing is commonly used in cutting-edge research in machine learning, where the use of edge nodes to offload computational and storage tasks from a central server can effectively improve training efficiency. (1) We consider federated learning and edge computing scenario to keep private data local instead of being uploaded to the central server, which helps to protect users’ privacy. The public auditability feature of blockchain allows other nodes to verify the aggregation results, preventing dishonest behaviors of aggregation nodes
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