Split Learning

Split learning is a distributed machine learning approach aiming to train models collaboratively across multiple devices while preserving data privacy by avoiding raw data sharing. Current research focuses on enhancing privacy through techniques like differential privacy and homomorphic encryption, optimizing model architectures (including neural networks, vision transformers, and large language models) for efficient split training, and addressing challenges like biased gradients and communication overhead in heterogeneous networks. This approach holds significant promise for applications requiring both collaborative model training and stringent data protection, such as those in healthcare, satellite communications, and edge computing.