Encryption Method

Encryption methods are crucial for protecting sensitive data used in machine learning, particularly in distributed settings like federated learning. Current research focuses on developing efficient and robust encryption techniques compatible with various model architectures, including convolutional neural networks (CNNs), vision transformers (ViTs), and large language models (LLMs), often employing techniques like homomorphic encryption, secret sharing, and secure aggregation to balance privacy with computational efficiency. These advancements are vital for enabling the secure deployment of machine learning in sensitive applications like healthcare, finance, and IoT, addressing concerns about data breaches and unauthorized access.