Abstract

Worldwide Internet of Medical Things (IoMT) sector has been experiencing a vertiginous rate of evolution in the past few years, going from a little wristwatch to a large aeroplane. Smart Health Care (SHC) systems utilize innovation technologies like IoMT, cloud edge computing and Artificial Intelligence (AI). With connected wearable devices and quick replies, SHC improves healthcare management by making it more efficient, convenient, and personalized. Deep Learners (DL) in the cloud are trained using the data collected from these devices. These servers have a lot of memory and a lot of processing expenditures. By utilizing a decentralized architecture known as Federated Learning (FL), several edge clients can work together to build a unified DL model effectively protecting the privacy of their own data. When a model loses all memory of its prior training data after receiving fresh input is referred as Catastrophic Forgetting (CF) problem. When the data distribution on each device changes over time, this can happen in a FL environment. As a Federated Increment Learning (FIL) system, Re-Fed can reduce CF by letting all clients each client remembers past samples based on how important. However, discrepant arrival times of the new task and data from the malfunctioning clients are not handled by Re-Fed FIL.This paper propose a Federated Improved Re-Fed Incremental Learning (FIRFIL) which handle the above issue through temporally weighted aggregation. In this research, a Time-Invariant Stochastic Spiking Long Short Term Memory (TISSLSTM) is used in a FIRFIL scenario. Internet of Things (IoT) devices sent the data acquired from various wearable sensors including those for blood sugar, heart rate, and chest readings to edge devices equipped with TISSLSTM for training. In FIRFIL, every edge device uses its own private data set to train a local model. A centralized server receives the local models and merges them into one global model. Next, the edge devices are updated with trained global model once again. This loop is continued until either the global model converges, or specific amounts of training rounds have passed. Next, we use the trained model to forecast client-specific diseases based on incoming data. A temporal weighted aggregation model in the server handle temporally variants data from clients. The proposed model is simply known as FIRFIL-TISSLSTM. At last, the test result demonstrate that the proposed model achieves 95.09%, 95.25% and 94.28% of accuracy on Comprehensive Heart Disease Dataset, UCI Heart Disease Dataset and Kaggle Heart Disease Dataset respectively outperforming traditional models. Also, the proposed model records lower energy consumption values 89.7J, 80.3J, 86.1J of energy consumption and reduced latency values of 173.8ms, 162.5ms, 168.4ms of latency on same datasets highlight its efficiency compared to other standard models.

Authors

G. Sasikala¹, G. Kalpana²
Dr. G. R. Damodaran College of Science, India¹, Sri Ramakrishna College of Arts and Science for Women, India²

Keywords

Internet of Medical Things (IoMT), Artificial Intelligence (AI), Smart Healthcare (SHC), Federated Incremental Learning (FIL), Time Invariant, Wearable Devices