Federated Learning with Efficient Local Adaptation for Realized Volatility Prediction

Abstract

Financial markets present unique challenges for Federated Learning (FL) due to fragmented datasets, dynamic participation, and the critical need for precise and reliable predictions. Isolated local datasets often fail to capture the full spectrum of market dynamics, blocking accurate realized volatility predictions. Unlike traditional FL methods that focus on improving convergence during the training process, we propose Federated Learning with Adaptive Robustness and Efficiency for Local Adaptation (FLARE-LA), a novel framework designed to optimize predictive performance after the global training phase. FLARE-LA leverages Taylor-based local linearization and probabilistic optimization to efficiently adapt global models to local data distributions, enabling fast responsiveness to new market conditions. This adaptability ensures trained local models align with real-world scenarios, making FLARE-LA particularly suited to dynamic financial applications. Extensive experimental evaluations demonstrate FLARE-LA's superior performance, showcasing its ability to significantly enhance post-FL outcomes compared to state-of-the-art FL algorithms. The results underscore FLARE-LA's unique capability to drive advancements in financial forecasting and other high-stakes, rapidly evolving domains.