Federated Learning for Securing Data Access and its Applications in Healthcare

Federated learning (FL) has captured the attention of professionals worldwide by addressing the challenge of managing vast amounts of data in a decentralized manner. In sectors such as healthcare, where data involves sensitive, confidential information, even a minor breach can significantly impact an individual’s privacy. The appeal of FL lies in its ability to leverage machine learning without exposing data to external environments. This approach ensures that data never leaves the user’s controlled environment while enhancing user comfort, making it particularly attractive in fields like healthcare. Recent advancements in FL have spurred its adoption as a means to bolster data security and access control, especially in sensitive domains like healthcare. This survey explores various FL techniques, specifically focusing on their applications in the healthcare industry. It provides insights into ongoing trends and attempts to anticipate the future trajectory of these technologies in this crucial field.