Collaborative Training

Collaborative training focuses on leveraging multiple datasets or computing resources to improve the performance and efficiency of machine learning models while addressing privacy concerns. Current research emphasizes federated learning, exploring strategies for optimal data partitioning and model aggregation across diverse datasets and resource-constrained devices, including the use of techniques like parameter-efficient fine-tuning and model splitting. This approach is significant for enhancing model accuracy and robustness in various applications, particularly where data is distributed across multiple parties or devices, such as in healthcare and personalized recommendations, while mitigating data leakage risks.