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Abstract Data-driven AI (artificial intelligence) models have become instrumental in drilling operations, offering potential efficiency gains through real-time predictions and decision support. However, while access to diverse datasets from different fields and companies can significantly enhance model performance, data sharing is often constrained by strict data security and privacy regulations. To overcome these challenges, we developed a federated learning framework that enables collaborative model training across decentralized datasets without compromising data confidentiality. This research presents an FL (federated learning)-based architecture specifically applied to address the lag in logging while drilling (LWD) data, where physical separation between LWD tools and the drill bit introduces delays in real-time formation information. Our FL framework employs multi-party secure aggregation, allowing each participating oil field to train models locally and share only encrypted model updates, which are then aggregated to create a global model. This approach not only preserves data privacy but also enhances model generalization by leveraging diverse data from multiple fields with varying geological and operational characteristics. In this study, we validate the FL framework through a case study involving three oil fields with heterogeneous datasets, each with unique feature characteristics and geological conditions. Results indicate that the FL-based model significantly improves predictive accuracy compared to independently trained models, reducing mean absolute error from 54.58 to 1.39 in the Volve field, from 27.11 to 2.49 in Bohai oilfield (BH), and from 40.91 to 3.88 in Xinjiang oilfield (XJ). These improvements demonstrate FL's ability to produce robust, generalizable models for drilling applications, offering a scalable and secure solution for cross-field collaboration in the petroleum industry. This study establishes a foundation for future applications of FL in addressing complex, data-intensive challenges in oil and gas operations.