In the rapidly evolving landscape of data science and machine learning, isolation transformers have emerged as a pivotal technology, particularly in scenarios requiring robust data privacy. This paper delves into the innovative design principles of isolation transformers and their significant contributions to distributed systems. We will explore how these models, originally proposed by researchers in the field of deep learning, facilitate secure data processing without the need for direct data sharing or exposure.
