CP-Decomposition Based Federated Learning with Shapley Value Aggregation
Chengqian Wu, Xuemei Fu, Xiangli Yang, Ruonan Zhao, Qidong Wu, Tinghua Zhang · 2023
Federated learning enables multiple data providers to collaborate on training models without exposing personal data. During the training process, frequent communication is required between the data provider and the central server, which puts great pressure on federated learning. To reduce the communication pressure of federated learning, we use the CP-decomposition processing model to reduce the size of data that needs to be transmitted during the communication process. In addition, we aggregate the global model based on the Shapley value, and eliminate nodes that are not beneficial to federated learning as soon as possible, which reduces the communication pressure and can stimulate the participating nodes and enhance the enthusiasm of participants in federated learning, thus improving the training results of the global model. We named the system CPSV, which stands for Federated learning of CP-decomposition models based on Shapley value aggregation. Numerous experiments on CPSV have shown that CPSV can motivate and supervise participating nodes to aggregate better global models while reducing the stress of federal learning communication.