Advancements and challenges of federated learning in medical imaging: a systematic literature review

Cancer diagnosis has entered an era where precision depends not only on image quality but on the intelligence that interprets it. While deep learning has revolutionized medical imaging, its reliance on centralized data limits collaboration due to privacy constraints and fragmented data ownership. Federated Learning (FL) offers a breakthrough enabling multiple institutions to co-train robust diagnostic models without sharing sensitive patient data. This survey provides a comprehensive cancer-specific synthesis of state-of-the-art FL applications in medical imaging, spanning five critical domains: lung, breast, brain, skin, and colorectal cancers. Beyond summarizing prior work, we uncover patterns in architecture choice (U-Net variants, Convolutional Neural Network (CNN)–Recurrent Neural Network(RNN) hybrids, dataset reuse, and state-of-the-art privacy frameworks such as homomorphic encryption and blockchain-backed consensus. We expose performance bottlenecks, heterogeneity risks, and critical absences of clinical deployment benchmarks. What emerges is not just a landscape of what has been done but a roadmap for what is needed to build secure, distributed, and clinically validated Artificial Intelligence (AI) systems. This work offers a foundation for researchers and healthcare technologists aiming to close the gap between research silos and real-world deployment.