Coot-Sine cosine algorithm enabled neural network model for financial distress prediction in federated learning

Abstract

Because of Covid-19 effect economic stability has been shaken and unemployment rates have increased in recent years, predicting financial conditions of consumers and providing a credit rating has become more important. So, to overcome these problems, Coot-Sine Cosine Algorithm (Coot-SCA_NN) is aimed to predict financial distress in federated learning (FL). Proposed method involves different entities, i.e., nodes and server. Process included in the proposed framework includes local training based on local data at each node, updates to server, model aggregation at server, and download of global model at the nodes, update training based on the downloaded global model and local model at every epoch of iteration. Here, in training model, input data is taken from dataset and then data augmentation model is carried out based on the mutual information. Finally, financial distress is predicted utilizing a neural network (NN) that is trained by the proposed optimization algorithm named Coot-SCA. At each node the information is processed and the details are recorded on the server for further processing. Proposed Coot-SCA_NN is derived by combining the Coot algorithm with Sine Cosine Algorithm (SCA). A major contribution is made through local renewal and consolidation of the service performed by replacing CAViaR. The performance of the proposed method is analyzed using Financial Distress Prediction dataset and the proposed method has the accuracy of 0.924, MSE of 0.083, RMSE of 0.289, Loss of 0.079, and MAP of 0.910.