Multicriteria client selection model using class topper optimization based optimal federated learning for healthcare informatics

Quality of life (QoL) of patients has grown as a result of thoughtful medical care systems where many stakeholders remotely review records. Data privacy is highly at risk due to open communication channels, which also has an impact on how models are trained using centralized servers' acquired data. An emerging idea called federated learning (FL) provides a workable remedy to this problem. There hasn't been a comprehensive or in-depth study of FL in the field of health informatics (HI), in contrast to previous studies that mainly focused on the role of FL in diverse applications. In this proposed approach, a Class Topper Optimization (CTO) based federated learning approach is developed. Clinical data's uploaded by clients are taken as input for this proposed work. Stratified sampling is employed to select clients according to their metadata, preventing contacts with clients that aren't relevant. In this paper, clients are selected based on the CTO approach utilizing a variety of criteria's. The server then receives the newly created parameters from each selected clients, which is then utilized for the training process of the local model. Two different algorithms named as Artificial Neural Network (ANN) and Convolutional Neural Network (CNN) are utilized as a local model to train the homogeneous client. The global model is further improved periodically by utilizing the updates from the locally trained instances. Long Short Term Memory (LSTM) is employed as a global model here. The proposed approach achieves 93% accuracy and 92% precision. Thus, the proposed optimization based client selection approach is the best choice for federated learning.