Application of Artificial Intelligence-Driven Federated Learning Based on Machine Learning and Deep Learning in Medicine 

Abstract

Currently, artificial intelligence (AI) technology is developing rapidly. Machine learning and deep learning are algorithms in the field of AI, and their combined use in federated learning is becoming increasingly common in medical research. The emergence of federated learning technology aims to train machine learning and deep learning algorithms across multiple distributed devices or servers. Federated learning has greatly promoted the development of AI in the medical field. The core of this approach is to construct complex and accurate models by automatically learning and extracting useful features from large amounts of data from multiple data sources, thereby building models with both high accuracy and precision. The widespread adoption of federated learning is bound to lead to breakthrough advances in areas such as precision medicine, clinical decision support, new drug development, medical image recognition, medical language processing, and medical speech recognition. This chapter draws on the author’s experience in big data medical modeling and validation from multiple data sources to introduce algorithms and operational modes in the field of federated learning, offering a glimpse into the promising future of the intelligent world.

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