Federated Learning in Medical Image Analysis: A Review of Shanghai's 2014-2024 Healthcare Innovations and Data Privacy Advances

Federated Learning (FL) has emerged as a significant approach to enhance data privacy, particularly in sensitive sectors like healthcare. By enabling collaborative machine learning across decentralized devices, FL ensures that data remains local, minimizing risks associated with centralized data storage. This paper provides an overview of data privacy advancements in FL, focusing on privacy-preserving techniques such as differential privacy and homomorphic encryption. While FL offers promising solutions for secure model training, it faces challenges like model inversion attacks, data poisoning, and regulatory concerns. Ethical issues regarding patient data sharing, governance, and ownership are also highlighted. Future research directions include optimizing quantization strategies within privacy-preserving frameworks and expanding compatibility with various neural network architectures. The integration of FL with technologies like blockchain is anticipated to further strengthen privacy standards and revolutionize healthcare applications. The ongoing development of federated learning frameworks will significantly impact the field of medical image analysis, providing enhanced privacy protections while improving model performance.