点击次数：

影响因子：6.6

所属单位：福建理工大学计算机科学与数学学院

发表刊物：Applied Soft Computing

项目来源：福建省自然科学基金

摘要：This paper introduces the Federated Three-Way Decision System (F3WDS), a novel framework for multicloud resource scheduling that integrates federated learning with the three-way decision theory to address the challenges of resource heterogeneity, decision uncertainty, and data privacy. By combining privacy-preserving collaborative learning with nuanced decision-making (positive, boundary, and negative regions), the F3WDS optimizes resource allocation across multiple cloud providers while adhering to strict data sovereignty requirements. We provide rigorous theoretical guarantees, including convergence analysis, privacy bounds, and performance bounds, to demonstrate the reliability of the system. Extensive experiments on synthetic and real-world datasets demonstrate that F3WDS achieves significant improvements over state-of-the-art methods: 5%–14% higher resource utilization, 60% lower privacy loss, and 30% reduced cross-cloud latency. The framework’s scalability, robustness to stragglers, and favorable privacy-utility trade-off make it a solution for privacy-sensitive multicloud environments, with implications for future research on distributed computing and privacy-aware resource management.

论文类型：期刊论文

学科门类：工学

卷号：183

期号：11

是否译文：否

发表时间：2025-07-22

收录刊物：SCI

发布期刊链接：https://www-sciencedirect-com-443.webvpn.fjut.edu.cn/science/article/abs/pii/S1568494625009457

第一作者：Chunmao Jiang

合写作者：Lirun Su