Bert-based Federated Learning to Rank Towards News Retrieval
C. G. Gan · 2023
With the development of machine learning technology, machine learning based information retrieval model plays a vital role in news retrieval tasks. In this paper, we study the integration of federated learning with the Bert model for information retrieval tasks. Our focus is on leveraging the advantages of the Bert model, such as its ability to learn rich semantic representations and handle query ambiguity. A key challenge we address is the preservation of data privacy during the model training process. To overcome this, we propose the utilization of federated learning, enabling training and updates while safeguarding sensitive information. We propose a framework that combines federated learning with the Bert model for information retrieval tasks. Through experiments, we demonstrate the effectiveness of our approach, with the results showcasing its ability to achieve both high model performance and information retrieval capability while ensuring data privacy. In summary, our paper contributes to the field by investigating the integration of federated learning with the Bert model for information retrieval tasks. We propose a framework, address the challenge of data privacy, and through experiments, demonstrate the effectiveness of our proposed method.