Khan, A.R.; Manzoor, H.U.; Ayaz, F.; Imran, M.A.; Zoha, A. A Privacy and Energy-Aware Federated Framework for Human Activity Recognition. Sensors2023, 23, 9339.
Khan, A.R.; Manzoor, H.U.; Ayaz, F.; Imran, M.A.; Zoha, A. A Privacy and Energy-Aware Federated Framework for Human Activity Recognition. Sensors 2023, 23, 9339.
Khan, A.R.; Manzoor, H.U.; Ayaz, F.; Imran, M.A.; Zoha, A. A Privacy and Energy-Aware Federated Framework for Human Activity Recognition. Sensors2023, 23, 9339.
Khan, A.R.; Manzoor, H.U.; Ayaz, F.; Imran, M.A.; Zoha, A. A Privacy and Energy-Aware Federated Framework for Human Activity Recognition. Sensors 2023, 23, 9339.
Abstract
Human activity recognition (HAR) using wearable sensors is gaining popularity in the healthcare domain, enabling continuous monitoring and timely interventions. The conventional HAR systems are centralised and need data sharing with a server for processing and model training. However, this data sharing leads to various challenges, such as privacy concerns, increased communication and storage costs, inefficient computational algorithms, data scarcity due to unwillingness to share, and difficulty achieving high accuracy and personalised models simultaneously. To overcome these challenges, this paper introduces a privacy and energy-aware federated learning (FL) framework for model training without data sharing. The proposed scheme incorporates spiking neural networks (SNNs), which are particularly suited for resource-constrained devices due to their event-driven information processing. The key idea is to synergise the strengths of SNNs and long short-term memory (LSTM) to create a hybrid model for robust HAR. The proposed hybrid spiking LSTM (S-LSTM) model uses the LSTM as an input layer to capture temporal dependencies efficiently, followed by spiking layers that replace the typical activation using leaky integrate-and-fire neurons. We employ the combination of surrogate gradient learning and backpropagation through time (BPTT) to update the weights of SNN layers in a supervised manner. The performance of the proposed model is evaluated using two publicly available datasets for both indoor and outdoor scenarios. The results show that the proposed S-LSTM performs much better than spike convolutional neural networks (S-CNN) and simple CNN, achieving the accuracy of 97% and 87% for indoor and outdoor datasets, respectively. Personalisation, also known as fine-tuning the global model using local data, is crucial in HAR. It allows the model to adapt to individual behaviour and preferences, making predictions more accurate. The results also show that personalisation improves the performance of participants up to 9% on average. Additionally, analysis with 50% random client participation reduces communication costs by 50% with minimal impact on accuracy.
Engineering, Electrical and Electronic Engineering
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
