mlsanityData to 7 Oct 2026

Joint Power Control and Data Size Selection for Over-the-Air Computation Aided Federated Learning

arXiv 2308.09072 ↗First posted 17 Aug 2023cs.LGcited by 0

Not on the map. Why?

Abstract (arXiv)

Federated learning (FL) has emerged as an appealing machine learning approach to deal with massive raw data generated at multiple mobile devices, {which needs to aggregate the training model parameter of every mobile device at one base station (BS) iteratively}. For parameter aggregating in FL, over-the-air computation is a spectrum-efficient solution, which allows all mobile devices to transmit their parameter-mapped signals concurrently to a BS. Due to heterogeneous channel fading and noise, there exists difference between the BS's received signal and its desired signal, measured as the mean-squared error (MSE). To minimize the MSE, we propose to jointly optimize the signal amplification factors at the BS and the mobile devices as well as the data size (the number of data samples involved in local training) at every mobile device. The formulated problem is challenging to solve due to its non-convexity. To find the optimal solution, with some simplification on cost function and variable replacement, which still preserves equivalence, we transform the changed problem to be a bi-level problem equivalently. For the lower-level problem, optimal solution is found by enumerating every candidate solution from the Karush-Kuhn-Tucker (KKT) condition. For the upper-level problem, the optimal solution is found by exploring its piecewise convexity. Numerical results show that our proposed method can greatly reduce the MSE and can help to improve the training performance of FL compared with benchmark methods.

Key facts. No citing papers in the map yet (data through 7 Oct 2026). Builds on 35 of its 52 references that are in the map. First posted on arXiv 17 Aug 2023.

Builds on

35 of 52 references.

References · 35

  1. 2016Communication-Efficient Learning of Deep Networks from Decentralized Datacited by 7,592
  2. 2019Advances and Open Problems in Federated Learningcited by 3,021
  3. 2021Convex optimizationcited by 2,479
  4. 2018Adaptive Federated Learning in Resource Constrained Edge Computing Systemscited by 408
  5. 2019A Joint Learning and Communications Framework for Federated Learning over Wireless Networkscited by 262
  6. 2018Federated Learning via Over-the-Air Computationcited by 215
  7. 2021Achieving Linear Speedup with Partial Worker Participation in Non-IID Federated Learningcited by 192
  8. 2018Broadband Analog Aggregation for Low-Latency Federated Edge Learning (Extended Version)cited by 186
  9. 2019Energy Efficient Federated Learning Over Wireless Communication Networkscited by 181
  10. 2019On the Linear Speedup Analysis of Communication Efficient Momentum SGD for Distributed Non-Convex Optimizationcited by 178
  11. 2020Federated Learning with Compression: Unified Analysis and Sharp Guaranteescited by 159
  12. 2019Local SGD with Periodic Averaging: Tighter Analysis and Adaptive Synchronizationcited by 106
23 more
  1. 2020Client Selection and Bandwidth Allocation in Wireless Federated Learning Networks: A Long-Term Perspectivecited by 102
  2. 2020Joint Device Scheduling and Resource Allocation for Latency Constrained Wireless Federated Learningcited by 90
  3. 2020Convergence Time Optimization for Federated Learning over Wireless Networkscited by 81
  4. 2018In-Edge AI: Intelligentizing Mobile Edge Computing, Caching and Communication by Federated Learningcited by 80
  5. 2020HFEL: Joint Edge Association and Resource Allocation for Cost-Efficient Hierarchical Federated Edge Learningcited by 74
  6. 2020Over-the-Air Federated Learning from Heterogeneous Datacited by 72
  7. 2020Privacy For Free: Wireless Federated Learning Via Uncoded Transmission With Adaptive Power Controlcited by 62
  8. 2021Optimized Power Control Design for Over-the-Air Federated Edge Learningcited by 45
  9. 2019Wireless Federated Distillation for Distributed Edge Learning with Heterogeneous Datacited by 39
  10. 2020Federated Learning via Intelligent Reflecting Surfacecited by 38
  11. 2021Dynamic Scheduling for Over-the-Air Federated Edge Learning with Energy Constraintscited by 34
  12. 2020Reconfigurable Intelligent Surface Enabled Federated Learning: A Unified Communication-Learning Design Approachcited by 32
  13. 2021Bias-Variance Reduced Local SGD for Less Heterogeneous Federated Learningcited by 30
  14. 2021Learning Rate Optimization for Federated Learning Exploiting Over-the-air Computationcited by 27
  15. 2019Over-the-Air Computation Systems: Optimization, Analysis and Scaling Lawscited by 25
  16. 2019Cell-Free Massive MIMO for Wireless Federated Learningcited by 25
  17. 2021Convergence Analysis and System Design for Federated Learning over Wireless Networkscited by 25
  18. 2022Interference Management for Over-the-Air Federated Learning in Multi-Cell Wireless Networkscited by 15
  19. 2020Hybrid Beamforming for Massive MIMO Over-the-Air Computationcited by 12
  20. 2021Wirelessly Powered Federated Edge Learning: Optimal Tradeoffs Between Convergence and Power Transfercited by 10
  21. 2022Knowledge-Guided Learning for Transceiver Design in Over-the-Air Federated Learningcited by 10
  22. 2022Accelerating Distributed Optimization via Over-the-Air Computingcited by 4
  23. 2022Performance-Oriented Design for Intelligent Reflecting Surface Assisted Federated Learningcited by 2

Built on by

Not cited yet.