A Comprehensive Review of Federated Learning Applications in Medical Image Analysis in Shenzhen, China: Advancements, Challenges, and Future Directions (2018-2024)

Federated learning (FL) has emerged as a transformative approach in medical image analysis, enabling collaborative model training while ensuring the privacy of sensitive patient data. This review explores the integration of FL in medical imaging applications, particularly in Shenzhen, China, from 2018 to 2024. FL facilitates decentralized data processing, allowing healthcare institutions to collaborate without sharing private medical information, thus enhancing diagnostic accuracy and improving patient outcomes. The review also discusses the impact of the COVID-19 pandemic, which accelerated the adoption of FL in urgent health scenarios. Despite its promising potential, the implementation of FL in medical imaging faces challenges, including legal and ethical concerns related to data privacy, technical hurdles in model deployment, and infrastructure limitations. The paper highlights future directions for FL, such as the integration of blockchain, hybrid models, and advanced data preprocessing techniques, which could further improve the security, scalability, and applicability of FL in healthcare. As technology continues to evolve, FL is expected to play a key role in reshaping the landscape of medical imaging and data-driven healthcare practices.