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[2602.18627] Federated Learning-Assisted Optimization of Mobile Transmission with Digital Twins
Summary
This paper presents a novel framework for optimizing mobile transmission using federated learning and digital twins, ensuring privacy while improving efficiency in scheduling tasks.
Why It Matters
As mobile devices increasingly rely on shared bandwidth and channel time slots, maintaining user privacy becomes crucial. This research introduces a method that allows for efficient scheduling without exposing sensitive information, which is vital for enhancing mobile communication systems.
Key Takeaways
- Introduces a federated learning framework for mobile transmission optimization.
- Ensures privacy of user data while improving scheduling efficiency.
- Demonstrates significant reductions in transmission time and energy violations.
Computer Science > Networking and Internet Architecture arXiv:2602.18627 (cs) [Submitted on 20 Feb 2026] Title:Federated Learning-Assisted Optimization of Mobile Transmission with Digital Twins Authors:Mohammad Heydari, Terence D. Todd, Dongmei Zhao, George Karakostas View a PDF of the paper titled Federated Learning-Assisted Optimization of Mobile Transmission with Digital Twins, by Mohammad Heydari and 3 other authors View PDF HTML (experimental) 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 transmissi...