Federated Learning for Task and Resource Allocation in Wireless High-Altitude Balloon Networks
نویسندگان
چکیده
In this article, the problem of minimizing energy and time consumption for task computation transmission in mobile-edge computing-enabled balloon networks is investigated. considered network, high-altitude balloons (HABs), acting as flying wireless base stations, can use their powerful computational capabilities to process tasks offloaded from associated users. Since data size each user’s varies over time, HABs must dynamically adjust resource allocation schemes meet users’ needs. This posed an optimization problem, whose goal minimize by adjusting user association, service sequence, schemes. To solve a support vector machine (SVM)-based federated learning (FL) algorithm proposed determine association proactively. The SVM-based FL method enables cooperatively build SVM model that all associations without any transmissions either historical or other HABs. Given predictions optimal sequence be optimized so weighted sum consumption. Simulations with real-city cellular traffic show reduce users up 15.4% compared conventional centralized method.
منابع مشابه
Resource Allocation Techniques for High Altitude Platforms
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ژورنال
عنوان ژورنال: IEEE Internet of Things Journal
سال: 2021
ISSN: ['2372-2541', '2327-4662']
DOI: https://doi.org/10.1109/jiot.2021.3080078