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Cell-Free Massive MIMO for Wireless Federated Learning

arXiv 1909.12567 ↗First posted 27 Sep 2019eess.SP cs.IT math.ITcited by 25

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Abstract (arXiv)

This paper proposes a novel scheme for cell-free massive multiple-input multiple-output (CFmMIMO) networks to support any federated learning (FL) framework. This scheme allows each instead of all the iterations of the FL framework to happen in a large-scale coherence time to guarantee a stable operation of an FL process. To show how to optimize the FL performance using this proposed scheme, we consider an existing FL framework as an example and target FL training time minimization for this framework. An optimization problem is then formulated to jointly optimize the local accuracy, transmit power, data rate, and users' processing frequency. This mixed-timescale stochastic nonconvex problem captures the complex interactions among the training time, and transmission and computation of training updates of one FL process. By employing the online successive convex approximation approach, we develop a new algorithm to solve the formulated problem with proven convergence to the neighbourhood of its stationary points. Our numerical results confirm that the presented joint design reduces the training time by up to 55% over baseline approaches. They also show that CFmMIMO here requires the lowest training time for FL processes compared with cell-free time-division multiple access massive MIMO and collocated massive MIMO.

Key facts. Cited by 25 papers in the map (data through 14 Oct 2026). First posted on arXiv 27 Sep 2019.

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Citing papers per year: 2021: 9; 2022: 7; 2023: 4; 2024: 4; 2025: 1.

25 papers.

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  1. 2021Edge Artificial Intelligence for 6G: Vision, Enabling Technologies, and Applicationscited by 97
  2. 2021Edge Federated Learning Via Unit-Modulus Over-The-Air Computationcited by 16
  3. 2021Joint Optimization of Communications and Federated Learning Over the Aircited by 14
  4. 2022Multi-Edge Server-Assisted Dynamic Federated Learning with an Optimized Floating Aggregation Pointcited by 10
  5. 2022Time-triggered Federated Learning over Wireless Networkscited by 10
  6. 2022Convergence Acceleration in Wireless Federated Learning: A Stackelberg Game Approachcited by 10
  7. 2022Service Delay Minimization for Federated Learning over Mobile Devicescited by 9
  8. 2021Over-the-Air Federated Multi-Task Learning Over MIMO Multiple Access Channelscited by 8
  9. 2023Vertical Federated Learning over Cloud-RAN: Convergence Analysis and System Optimizationcited by 7
  10. 2021FedFog: Network-Aware Optimization of Federated Learning over Wireless Fog-Cloud Systemscited by 6
  11. 2021How Does Cell-Free Massive MIMO Support Multiple Federated Learning Groups?cited by 3
  12. 2021Optimization-Based GenQSGD for Federated Edge Learningcited by 3
13 more
  1. 2021An Optimization Framework for Federated Edge Learningcited by 3
  2. 2024Adaptive and Parallel Split Federated Learning in Vehicular Edge Computingcited by 3
  3. 2021Federated learning and next generation wireless communications: A survey on bidirectional relationshipcited by 2
  4. 2022Over-The-Air Federated Learning Over Scalable Cell-free Massive MIMOcited by 2
  5. 2024Joint Energy and Latency Optimization in Federated Learning over Cell-Free Massive MIMO Networkscited by 2
  6. 2022Completion Time Minimization of Fog-RAN-Assisted Federated Learning With Rate-Splitting Transmissioncited by 1
  7. 2023GQFedWAvg: Optimization-Based Quantized Federated Learning in General Edge Computing Systemscited by 1
  8. 2023Wirelessly Powered Federated Learning Networks: Joint Power Transfer, Data Sensing, Model Training, and Resource Allocationcited by 1
  9. 2024Accelerating Energy-Efficient Federated Learning in Cell-Free Networks with Adaptive Quantizationcited by 1
  10. 2022Random Orthogonalization for Federated Learning in Massive MIMO Systemscited by 0
  11. 2023Joint Power Control and Data Size Selection for Over-the-Air Computation Aided Federated Learningcited by 0
  12. 2024Online Client Scheduling and Resource Allocation for Efficient Federated Edge Learningcited by 0
  13. 2025Towards Seamless Hierarchical Federated Learning under Intermittent Client Participation: A Stagewise Decision-Making Methodologycited by 0