Electronic Resource
Channel and Gradient-Importance Aware Device Scheduling for Over-the-Air Federated Learning
| العنوان: | Channel and Gradient-Importance Aware Device Scheduling for Over-the-Air Federated Learning |
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| المؤلفون: | Sun, Yuchang, Lin, Zehong, Mao, Yuyi, Jin, Shi, Zhang, Jun |
| بيانات النشر: | Institute of Electrical and Electronics Engineers Inc. 2024 |
| نوع الوثيقة: | Electronic Resource |
| مستخلص: | Federated learning (FL) is a popular privacy-preserving distributed training scheme, where multiple devices collaborate to train machine learning models by uploading local model updates. To improve communication efficiency, over-the-air computation (AirComp) has been applied to FL, which leverages analog modulation to harness the superposition property of radio waves such that numerous devices can upload their model updates concurrently for aggregation. However, the uplink channel noise incurs considerable model aggregation distortion, which is critically determined by the device scheduling and compromises the learned model performance. In this paper, we propose a probabilistic device scheduling framework for over-the-air FL, named PO-FL, to mitigate the negative impact of channel noise, where each device is scheduled according to a certain probability and its model update is reweighted using this probability in aggregation. We prove the unbiasedness of this aggregation scheme and demonstrate the convergence of PO-FL on both convex and non-convex loss functions. Our convergence bounds unveil that the device scheduling affects the learning performance through the communication distortion and global update variance. Based on the convergence analysis, we further develop a channel and gradient-importance aware algorithm to optimize the device scheduling probabilities in PO-FL. Extensive simulation results show that the proposed PO-FL framework with channel and gradient-importance awareness achieves faster convergence and produces better models than baseline methods. IEEE |
| مصطلحات الفهرس: | Atmospheric modeling, Channel awareness, Convergence, Data models, Device scheduling, Federated learning (FL), Gradient importance, Optimal scheduling, Over-the-air computation (AirComp), Performance evaluation, Servers, Training, Article |
| URL: | |
| الاتاحة: | Open access content. Open access content |
| ملاحظة: | English |
| Other Numbers: | HNK oai:repository.hkust.edu.hk:1783.1-135020 IEEE Transactions on Wireless Communications, v. 23, (7), July 2024, article number 10341307, p. 6905-6920 1536-1276 1558-2248 1430646137 |
| المصدر المساهم: | HONG KONG UNIV OF SCI & TECH, THE From OAIster®, provided by the OCLC Cooperative. |
| رقم الانضمام: | edsoai.on1430646137 |
| قاعدة البيانات: | OAIster |
| الوصف غير متاح. |