Hierarchical Learning: A Hybrid of Federated Learning and Personalization Fine-Tuning

Hierarchical Federated Learning (FL) presents a novel approach that combines global model training with localized personalization fine-tuning to enhance the predictive accuracy of decentralized machine learning systems. Traditional FL methods, which allow multiple clients to collaboratively train a global model without sharing raw data, are hindered by issues such as non-independent and identically distributed (non-IID) data, communication overhead, and limited generalization across diverse client datasets. This study proposes a hierarchical model that mitigates these challenges by incorporating a global model, trained using the Federated Averaging (FedAvg) algorithm, and applying client-specific fine-tuning to improve local model performance. The experiment conducted on a movie recommendation system demonstrates that this hierarchical approach significantly reduces the global model’s error while offering personalized improvements on client-specific datasets. Results show an average Root Mean Squared Error (RMSE) reduction of 0.0460 following local personalization. This hybrid approach not only enhances model accuracy but also preserves data privacy and increases scalability, making it a promising solution for decentralized recommendation systems.