DECENTRALISED ARTIFICIAL INTELLIGENCE (AI)/MACHINE LEARNING TRAINING SYSTEM

摘要

A decentralized training platform is described for training an Artificial Intelligence (AI) model where training data (e.g., medical images) is distributed across multiple sites (nodes) and due to confidentiality, legal, or other reasons the data at each site is unable to be shared or leave the site and so cannot be copied to a central location for training. The method comprises training a teacher model locally at each node and then moving each of the teacher models to a central node and using these to train a student model using a transfer dataset. This may be facilitated by setting up the cloud service using inter-region peering connections between the nodes to make the nodes appear as a single cluster. In one variation the student module may be trained at each node using the multiple trained teacher models. In another variation we train multiple student models where each student model is trained by each teacher model at the node the teacher model was trained on, and once the plurality of student models are trained, an ensemble model is generated from the plurality of trained student models. Loss function weighting and node under sampling to enable load balancing may be used to improve accuracy and time/cost efficiency.