Scalable Multi-Party Collaborative Data Mining Based on Federated Learning

This paper proposes a federated learning-based method for multi-party collaborative mining and heterogeneous data source fusion. The goal is to address the shortcomings of traditional centralized learning in data privacy protection and cross-domain collaboration. To tackle the heterogeneity problem among multiple distributed data sources, the proposed method uses the federated learning framework to achieve collaborative training across different data sources. This approach avoids direct data transmission, effectively protecting data privacy. By transmitting model parameters to a central server for aggregation, the method enables efficient model fusion and collaborative learning across multiple data sources, significantly improving learning performance in non-i.i.d. data environments. Experimental results show that the proposed model demonstrates high accuracy and stability when handling heterogeneous data sources in multi-party collaborative learning. It outperforms other existing federated learning methods, such as FedAvg and SCAFFOLD. The model is able to ensure data privacy while improving data fusion efficiency and model generalization. This approach holds promising application potential. This study provides an innovative solution to the challenges of multi-party collaborative mining and data privacy protection, particularly in fields such as healthcare, financial risk control, and intelligent manufacturing, where data security is paramount. It offers technological support for real-world data sharing and cross-domain collaboration.