# Cell-Free Massive MIMO for Wireless Federated Learning

arXiv: https://arxiv.org/abs/1909.12567 · HTML: https://mlsanity.com/paper/1909.12567/

## Key facts

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

## Built on by (most cited first, 25 of 25)

- [Edge Artificial Intelligence for 6G: Vision, Enabling Technologies, and Applications](https://mlsanity.com/paper/2111.12444/) (2021)
- [Edge Federated Learning Via Unit-Modulus Over-The-Air Computation](https://mlsanity.com/paper/2101.12051/) (2021)
- [Joint Optimization of Communications and Federated Learning Over the Air](https://mlsanity.com/paper/2104.03490/) (2021)
- [Multi-Edge Server-Assisted Dynamic Federated Learning with an Optimized Floating Aggregation Point](https://mlsanity.com/paper/2203.13950/) (2022)
- [Time-triggered Federated Learning over Wireless Networks](https://mlsanity.com/paper/2204.12426/) (2022)
- [Convergence Acceleration in Wireless Federated Learning: A Stackelberg Game Approach](https://mlsanity.com/paper/2209.06623/) (2022)
- [Service Delay Minimization for Federated Learning over Mobile Devices](https://mlsanity.com/paper/2205.09868/) (2022)
- [Over-the-Air Federated Multi-Task Learning Over MIMO Multiple Access Channels](https://mlsanity.com/paper/2112.13603/) (2021)
- [Vertical Federated Learning over Cloud-RAN: Convergence Analysis and System Optimization](https://mlsanity.com/paper/2305.06279/) (2023)
- [FedFog: Network-Aware Optimization of Federated Learning over Wireless Fog-Cloud Systems](https://mlsanity.com/paper/2107.02755/) (2021)
- [How Does Cell-Free Massive MIMO Support Multiple Federated Learning Groups?](https://mlsanity.com/paper/2107.09577/) (2021)
- [Optimization-Based GenQSGD for Federated Edge Learning](https://mlsanity.com/paper/2110.12987/) (2021)
- [An Optimization Framework for Federated Edge Learning](https://mlsanity.com/paper/2111.13526/) (2021)
- [Adaptive and Parallel Split Federated Learning in Vehicular Edge Computing](https://mlsanity.com/paper/2405.18707/) (2024)
- [Federated learning and next generation wireless communications: A survey on bidirectional relationship](https://mlsanity.com/paper/2110.07649/) (2021)
- [Over-The-Air Federated Learning Over Scalable Cell-free Massive MIMO](https://mlsanity.com/paper/2212.06482/) (2022)
- [Joint Energy and Latency Optimization in Federated Learning over Cell-Free Massive MIMO Networks](https://mlsanity.com/paper/2404.18287/) (2024)
- [Completion Time Minimization of Fog-RAN-Assisted Federated Learning With Rate-Splitting Transmission](https://mlsanity.com/paper/2206.01373/) (2022)
- [GQFedWAvg: Optimization-Based Quantized Federated Learning in General Edge Computing Systems](https://mlsanity.com/paper/2306.07497/) (2023)
- [Wirelessly Powered Federated Learning Networks: Joint Power Transfer, Data Sensing, Model Training, and Resource Allocation](https://mlsanity.com/paper/2308.04953/) (2023)
- [Accelerating Energy-Efficient Federated Learning in Cell-Free Networks with Adaptive Quantization](https://mlsanity.com/paper/2412.20785/) (2024)
- [Random Orthogonalization for Federated Learning in Massive MIMO Systems](https://mlsanity.com/paper/2210.09881/) (2022)
- [Joint Power Control and Data Size Selection for Over-the-Air Computation Aided Federated Learning](https://mlsanity.com/paper/2308.09072/) (2023)
- [Online Client Scheduling and Resource Allocation for Efficient Federated Edge Learning](https://mlsanity.com/paper/2410.10833/) (2024)
- [Towards Seamless Hierarchical Federated Learning under Intermittent Client Participation: A Stagewise Decision-Making Methodology](https://mlsanity.com/paper/2502.09303/) (2025)

## 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.