Federated Learning-Assisted Optimization of Mobile Transmission with Digital Twins

2026-02-20

AI Summary

This study addresses the challenge of protecting privacy-sensitive information—such as location and channel state—in mobile digital twin systems while optimizing wireless transmission under energy constraints. The goal is to minimize transmission latency and maximize data upload volume within a fixed time horizon. To this end, the work proposes the first federated optimization framework based on digital twins: the scheduler interacts solely with local digital twins, computes a global fractional solution via real-time distributed optimization, and then applies dependent rounding to generate physical-layer channel scheduling decisions—all without exposing raw private data. Experiments demonstrate that the method achieves millisecond-level end-to-end response on typical edge servers, significantly reduces task completion time, and adheres closely to bandwidth and energy constraints, thereby achieving a strong balance between privacy preservation and resource efficiency.

Technology Category

• Planning, Routing, and Scheduling: Optimization of Spatio-temporal Systems
• Machine Learning: Privacy
• Search and Optimization: Distributed Search

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Energy management for devices in mobile Web and WoT environments
• Security and Privacy: Data transparency and provenance
• User Modeling, Personalization and Recommendation: Federated recommendation systems and personalization

Abstract

A Digital Twin (DT) may protect information that is considered private to its associated physical system. For a mobile device, this may include its mobility profile, recent location(s), and experienced channel conditions. Online schedulers, however, typically use this type of information to perform tasks such as shared bandwidth and channel time slot assignments. In this paper, we consider three transmission scheduling problems with energy constraints, where such information is needed, and yet must remain private: minimizing total transmission time when (i) fixed-power or (ii) fixed-rate time slotting with power control is used, and (iii) maximizing the amount of data uploaded in a fixed time period. Using a real-time federated optimization framework, we show how the scheduler can iteratively interact only with the DTs to produce global fractional solutions to these problems, without the latter revealing their private information. Then dependent rounding is used to round the fractional solution into a channel transmission schedule for the physical systems. Experiments show consistent makespan reductions with near-zero bandwidth/energy violations and millisecond-order end-to-end runtime for typical edge server hardware. To the best of our knowledge, this is the first framework that enables channel sharing across DTs using operations that do not expose private data.

Problem

• Federated Learning
• Digital Twins
• Transmission Scheduling
• Privacy Preservation
• Energy Constraints

Innovation

• Federated Learning
• Digital Twin
• Privacy-Preserving Scheduling
• Dependent Rounding
• Mobile Transmission Optimization