Digital Over-the-Air Federated Learning in Multi-Antenna Systems
Not on the map. Why?
Abstract (arXiv)
In this paper, the performance optimization of federated learning (FL), when deployed over a realistic wireless multiple-input multiple-output (MIMO) communication system with digital modulation and over-the-air computation (AirComp) is studied. In particular, a MIMO system is considered in which edge devices transmit their local FL models (trained using their locally collected data) to a parameter server (PS) using beamforming to maximize the number of devices scheduled for transmission. The PS, acting as a central controller, generates a global FL model using the received local FL models and broadcasts it back to all devices. Due to the limited bandwidth in a wireless network, AirComp is adopted to enable efficient wireless data aggregation. However, fading of wireless channels can produce aggregate distortions in an AirComp-based FL scheme. To tackle this challenge, we propose a modified federated averaging (FedAvg) algorithm that combines digital modulation with AirComp to mitigate wireless fading while ensuring the communication efficiency. This is achieved by a joint transmit and receive beamforming design, which is formulated as an optimization problem to dynamically adjust the beamforming matrices based on current FL model parameters so as to minimize the transmitting error and ensure the FL performance. To achieve this goal, we first analytically characterize how the beamforming matrices affect the performance of the FedAvg in different iterations. Based on this relationship, an artificial neural network (ANN) is used to estimate the local FL models of all devices and adjust the beamforming matrices at the PS for future model transmission. The algorithmic advantages and improved performance of the proposed methodologies are demonstrated through extensive numerical experiments.
Key facts. Cited by 1 paper in the map (data through 7 Oct 2026). Builds on 35 of its 72 references that are in the map. First posted on arXiv 4 Feb 2023.
Builds on
35 of 72 references.
References · 35
- 2017Fashion-MNIST: a Novel Image Dataset for Benchmarking Machine Learning Algorithmscited by 4,283
- 2021Convex optimizationcited by 2,479
- 2019A Joint Learning and Communications Framework for Federated Learning over Wireless Networkscited by 262
- 2021Federated Learning for Internet of Things: A Comprehensive Surveycited by 247
- 2021Distributed Learning in Wireless Networks: Recent Progress and Future Challengescited by 125
- 2020One-Bit Over-the-Air Aggregation for Communication-Efficient Federated Edge Learning: Design and Convergence Analysiscited by 108
- 2020Over-the-Air Federated Learning from Heterogeneous Datacited by 72
- 2022Communication-Efficient Stochastic Zeroth-Order Optimization for Federated Learningcited by 59
- 2021Federated Dropout -- A Simple Approach for Enabling Federated Learning on Resource Constrained Devicescited by 56
- 2021Federated Learning over Wireless Device-to-Device Networks: Algorithms and Convergence Analysiscited by 51
- 2020Blind Federated Edge Learningcited by 42
- 2020Federated Learning via Intelligent Reflecting Surfacecited by 38
23 more
- 2021Federated Edge Learning with Misaligned Over-The-Air Computationcited by 28
- 2021Learning Rate Optimization for Federated Learning Exploiting Over-the-air Computationcited by 27
- 2020Adaptive Federated Learning With Gradient Compression in Uplink NOMAcited by 19
- 2022Joint Device Selection and Power Control for Wireless Federated Learningcited by 19
- 2022Parallel Successive Learning for Dynamic Distributed Model Training over Heterogeneous Wireless Networkscited by 18
- 2020Fast Convergence Algorithm for Analog Federated Learningcited by 16
- 2022Interference Management for Over-the-Air Federated Learning in Multi-Cell Wireless Networkscited by 15
- 2022Random Orthogonalization for Federated Learning in Massive MIMO Systemscited by 13
- 2021Over-the-Air Computation with DFT-spread OFDM for Federated Edge Learningcited by 9
- 2020Over-the-Air Computation Systems: Optimal Design with Sum-Power Constraintcited by 8
- 2021STAR-RIS Integrated Non-Orthogonal Multiple Access and Over-the-Air Federated Learning: Framework, Analysis, and Optimizationcited by 8
- 2020A Machine Learning Approach for Task and Resource Allocation in Mobile Edge Computing Based Networkscited by 7
- 2021User Scheduling for Federated Learning Through Over-the-Air Computationcited by 7
- 2021Over-the-Air Federated Multi-Task Learning Over MIMO Multiple Access Channelscited by 7
- 2020Balancing Rates and Variance via Adaptive Batch-Size for Stochastic Optimization Problemscited by 6
- 2020Cluster-Based Cooperative Digital Over-the-Air Aggregation for Wireless Federated Edge Learningcited by 6
- 2021Broadband Digital Over-the-Air Computation for Asynchronous Federated Edge Learningcited by 6
- 2022Energy and Spectrum Efficient Federated Learning via High-Precision Over-the-Air Computationcited by 6
- 2017Variance Reduced methods for Non-convex Composition Optimizationcited by 5
- 2020Amplify-and-Forward Relaying for Hierarchical Over-the-Air Computationcited by 3
- 2021CSIT-Free Model Aggregation for Federated Edge Learning via Reconfigurable Intelligent Surfacecited by 3
- 2023Simultaneously Transmitting and Reflecting (STAR) RIS Assisted Over-the-Air Computation Systemscited by 3
- 2021Sum-Rate-Distortion Function for Indirect Multiterminal Source Coding in Federated Learningcited by 2
Built on by
Citing papers per year: 2026: 1.
1 paper.