Collaborative Deep Learning

Collaborative deep learning focuses on training large, complex models across multiple devices or institutions, aiming to leverage distributed resources while addressing privacy and security concerns. Current research emphasizes efficient resource allocation strategies (like hybrid pipeline parallelism), robust model architectures for handling heterogeneous data (including multimodal approaches), and privacy-preserving techniques such as split learning and differential privacy. This field is significant for accelerating model training, enabling the development of more powerful AI systems in resource-constrained environments, and facilitating collaborative research across organizations while protecting sensitive data.