Federated and Transfer Learning for Cancer Detection Based on Image Analysis
Amine Bechar, Youssef Elmir, Yassine Himeur, Rafik Medjoudj, Abbes Amira
TL;DR
This survey analyzes how Federated Learning (FL) and Transfer Learning (TL) can advance cancer detection from medical images by addressing data privacy and limited labeled data. It details FL types (Horizontal, Vertical, Federated Transfer, and Decentralized) and TL techniques (fine-tuning, domain adaptation, and knowledge distillation), highlighting their complementary strengths and use cases in image-based CD. The work provides a taxonomy, surveys public datasets, and compares FL and TL in terms of performance, data requirements, privacy, and applicability across cancer types, while outlining challenges such as domain shift, 3D imaging, and data scarcity. It also presents case studies and future directions, including non-IID data handling, multimodal TL, and collaboration paradigms, to support practical deployment in clinical settings with privacy and efficiency in mind.
Abstract
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.