Multi-source Data Fusion Base on Block-Term Decomposition in Federated Learning

With the development of the Internet of Things technology and the popularization of artificial intelligence, the number of edge devices in the network is growing exponentially. These devices are generating a large amount of data all the time. However, the traditional methods of centralized processing of these data usually require huge storage space and cause high computation burden. In addition, massive data is generally diversified, making the unified processing of these heterogeneous data more complicated, and the utilization of data is low. In this paper, we propose a Block-Term Decomposition (BTD) based algorithm in federated learning to make a fully utilization of multi-source data on edges. For the fusion problem of heteroge-neous data without interaction in Federated Learning (FL), the proposed algorithm introduces BTD method to capture the multi-dimensional characteristics of heterogeneous data by constructing high-order tensors. Experimental results on IEMOCAP dataset show that the proposed algorithm improve calculation accuracy in a single node and under FL framework.