Deep learning (DL) has advanced computer-aided diagnosis, yet the limited data available at local medical centers and privacy concerns associated with centralized AI approaches hinder collaboration. Federated learning (FL) offers a privacy-preserving solution by enabling distributed DL training across multiple medical centers without sharing raw data. This article reviews research conducted from 2016 to 2024 on the use of FL in cancer detection and diagnosis, aiming to provide an overview of the field’s development. Studies show that FL effectively addresses privacy concerns in DL training across centers. Future research should focus on tackling data heterogeneity and domain adaptation to enhance the robustness of FL in clinical settings. Improving the interpretability and privacy of FL is crucial for building trust. This review promotes FL adoption and continued research to advance cancer detection and diagnosis and improve patient outcomes.