Federated Learning Using Coupled Tensor Train Decomposition

arxiv(2024)

引用 0|浏览12
暂无评分
摘要
Coupled tensor decomposition (CTD) can extract joint features from multimodal data in various applications. It can be employed for federated learning networks with data confidentiality. Federated CTD achieves data privacy protection by sharing common features and keeping individual features. However, traditional CTD schemes based on canonical polyadic decomposition (CPD) may suffer from low computational efficiency and heavy communication costs. Inspired by the efficient tensor train decomposition, we propose a coupled tensor train (CTT) decomposition for federated learning. The distributed coupled multi-way data are decomposed into a series of tensor trains with shared factors. In this way, we can extract common features of coupled modes while maintaining the different features of uncoupled modes. Thus the privacy preservation of information across different network nodes can be ensured. The proposed CTT approach is instantiated for two fundamental network structures, namely master-slave and decentralized networks. Experimental results on synthetic and real datasets demonstrate the superiority of the proposed schemes over existing methods in terms of both computational efficiency and communication rounds. In a classification task, experimental results show that the CTT-based federated learning achieves almost the same accuracy performance as that of the centralized counterpart.
更多
查看译文
AI 理解论文
溯源树
样例
生成溯源树,研究论文发展脉络
Chat Paper
正在生成论文摘要