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Device Scheduling for Relay-assisted Over-the-Air Aggregation in Federated Learning

arXiv 2312.12417 ↗First posted 15 Dec 2023cs.LGcited by 0

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Abstract (arXiv)

Federated learning (FL) leverages data distributed at the edge of the network to enable intelligent applications. The efficiency of FL can be improved by using over-the-air computation (AirComp) technology in the process of gradient aggregation. In this paper, we propose a relay-assisted large-scale FL framework, and investigate the device scheduling problem in relay-assisted FL systems under the constraints of power consumption and mean squared error (MSE). we formulate a joint device scheduling, and power allocation problem to maximize the number of scheduled devices. We solve the resultant non-convex optimization problem by transforming the optimization problem into multiple sparse optimization problems. By the proposed device scheduling algorithm, these sparse sub-problems are solved and the maximum number of federated learning edge devices is obtained. The simulation results demonstrate the effectiveness of the proposed scheme as compared with other benchmark schemes.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 13 of its 17 references that are in the map. First posted on arXiv 15 Dec 2023.

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13 of 17 references.

References · 13

  1. 2016Communication-Efficient Learning of Deep Networks from Decentralized Datacited by 7,669
  2. 2019FedPAQ: A Communication-Efficient Federated Learning Method with Periodic Averaging and Quantizationcited by 356
  3. 2018Federated Learning via Over-the-Air Computationcited by 219
  4. 2018Broadband Analog Aggregation for Low-Latency Federated Edge Learning (Extended Version)cited by 191
  5. 2020UVeQFed: Universal Vector Quantization for Federated Learningcited by 107
  6. 2020Client Selection and Bandwidth Allocation in Wireless Federated Learning Networks: A Long-Term Perspectivecited by 103
  7. 2022Split Learning over Wireless Networks: Parallel Design and Resource Managementcited by 75
  8. 2021Dynamic Scheduling for Over-the-Air Federated Edge Learning with Energy Constraintscited by 36
  9. 2022Gradient and Channel Aware Dynamic Scheduling for Over-the-Air Computation in Federated Edge Learning Systemscited by 20
  10. 2020Adaptive Federated Learning With Gradient Compression in Uplink NOMAcited by 19
  11. 2021Relay-Assisted Cooperative Federated Learningcited by 9
  12. 2021User Scheduling for Federated Learning Through Over-the-Air Computationcited by 7
1 more
  1. 2021Wireless Federated Learning (WFL) for 6G Networks -- Part II: The Compute-then-Transmit NOMA Paradigmcited by 6

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