Multi-source Heterogeneous Data Fusion Algorithm Based on Federated Learning

With the rapid advancement of science and technology, the number of edge devices with computing and storage capabilities, as well as the generated data traffic, continues to increase, making it difficult for the centralized processing mode centered on cloud computing to efficiently process the data generated by edge devices. Moreover, multimodal data is abundant due to the diversity of edge network devices and the ongoing improvement of data representation techniques. To make full use of heterogeneous data on edge devices and to tackle the "data communication barrier" problem caused by data privacy in edge computing, a multi-source heterogeneous data fusion technique based on Tucker decomposition is developed. The algorithm introduces tensor Tucker decomposition theory and realizes federated learning by constructing a high-order tensorwith heterogeneous spatial dimension characteristics to capture the high-dimensional features of heterogeneous data and solve the fusion problem of heterogeneous data assuming no interaction. Combining and remembering. This method, unlike its predecessors, can efficiently integrate multi-source heterogeneous data without data transfer, therefore overcoming privacy and security-related data communication concerns. Finally, the effectiveness of the technique is confirmed using the MOSI data set, and the shortcomings of the original federated learning algorithm are solved by the updated federated weighted average algorithm. Subjective criteria are used to calculate the data quality for the improved federated average approach based on AHP. We present Influence, an improved federated weighted average method for dealing with multi-source data from a data quality perspective.