Federated learning with three-way decisions for privacy-preserving multicloud resource scheduling

Date:2025-07-30 clicks:

Impact Factor:6.6

Affiliation of Author(s):福建理工大学计算机科学与数学学院

Journal:Applied Soft Computing

Funded by:福建省自然科学基金

Abstract: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.

Indexed by:Journal paper

Discipline:Engineering

Volume:183

Issue:11

Translation or Not:no

Date of Publication:2025-07-22

Included Journals:SCI

Links to published journals:https://www-sciencedirect-com-443.webvpn.fjut.edu.cn/science/article/abs/pii/S1568494625009457

First Author:Chunmao Jiang

Co-author:Lirun Su

Pre One:Anomaly detection in distributed systems based on Spatio-Temporal Causal Inference Next One:A Deep Reinforcement Learning Approach to Cloud Resource Optimization with Response Time Distributions