  This review article discusses the roles of federated learning (FL) and
transfer learning (TL) in cancer detection based on image analysis. These two
strategies powered by machine learning have drawn a lot of attention due to
their potential to increase the precision and effectiveness of cancer diagnosis
in light of the growing importance of machine learning techniques in cancer
detection. FL enables the training of machine learning models on data
distributed across multiple sites without the need for centralized data
sharing, while TL allows for the transfer of knowledge from one task to
another. A comprehensive assessment of the two methods, including their
strengths, and weaknesses is presented. Moving on, their applications in cancer
detection are discussed, including potential directions for the future.
Finally, this article offers a thorough description of the functions of TL and
FL in image-based cancer detection. The authors also make insightful
suggestions for additional study in this rapidly developing area.
