Artificial Intelligence (AI), including machine learning and deep learning models, is increasingly transforming oncology by providing powerful tools to analyze complex multidimensional data. However, developing reliable and generalizable models requires large-scale training datasets, which are often constrained by privacy regulations and the decentralized nature of medical data across institutions. Federated learning has recently emerged as a promising approach that enables collaborative model training across multiple sites without sharing raw data. This review presents the fundamental principles and architectural frameworks of federated learning, highlighting its strengths in protecting data privacy, improving model robustness, and facilitating the integration of multi-omics and multimodal datasets. Key applications in cancer detection, prognosis prediction, and treatment response modeling are discussed, demonstrating its potential to support clinical decision-making. Moreover, the review highlights major challenges in applying federated learning to oncology and outlines key directions to advance precision medicine, including the integration of multimodal data, foundation models, causal reasoning, and continual learning. With ongoing technological advancements, federated learning holds great promise to bridge artificial intelligence innovation and privacy protection in oncology.
Qi et al. (Tue,) studied this question.