Reduction in Data Imbalance for Client-Side Training in Federated Learning for the Prediction of Stock Market Prices

Abstract

The approach of federated learning (FL) addresses significant challenges, including access rights, privacy, security, and the availability of diverse data. However, edge devices produce and collect data in a non-independent and identically distributed (non-IID) manner. Therefore, it is possible that the number of data samples may vary among the edge devices. This study elucidates an approach for implementing FL to achieve a balance between training accuracy and imbalanced data. This approach entails the implementation of data augmentation in data distribution by utilizing class estimation and by balancing on the client side during local training. Secondly, simple linear regression is utilized for model training at the client side to manage the optimal computation cost to achieve a reduction in computation cost. To validate the proposed approach, the technique was applied to a stock market dataset comprising stocks (AAL, ADBE, ASDK, and BSX) to predict the day-to-day values of stocks. The proposed approach has demonstrated favorable results, exhibiting a strong fit of 0.95 and above with a low error rate. The R-squared values, predominantly ranging from 0.97 to 0.98, indicate the model’s effectiveness in capturing variations in stock prices. Strong fits are observed within 75 to 80 iterations for stocks displaying consistently high R-squared values, signifying accuracy. On the 100th iteration, the declining MSE, MAE, and RMSE (AAL at 122.03, 4.89, 11.04, respectively; ADBE at 457.35, 17.79, and 21.38, respectively; ASDK at 182.78, 5.81, 13.51, respectively; and BSX at 34.50, 4.87, 5.87, respectively) values corroborated the positive results of the proposed approach with minimal data loss.