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

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.