mlsanityData to 7 Oct 2026

Over-the-Air Computation Based on Balanced Number Systems for Federated Edge Learning

arXiv 2210.07012 ↗First posted 13 Oct 2022cs.IT cs.AI eess.SPcited by 9

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Abstract (arXiv)

In this study, we propose a digital over-the-air computation (OAC) scheme for achieving continuous-valued (analog) aggregation for federated edge learning (FEEL). We show that the average of a set of real-valued parameters can be calculated approximately by using the average of the corresponding numerals, where the numerals are obtained based on a balanced number system. By exploiting this key property, the proposed scheme encodes the local stochastic gradients into a set of numerals. Next, it determines the positions of the activated orthogonal frequency division multiplexing (OFDM) subcarriers by using the values of the numerals. To eliminate the need for precise sample-level time synchronization, channel estimation overhead, and channel inversion, the proposed scheme also uses a non-coherent receiver at the edge server (ES) and does not utilize a pre-equalization at the edge devices (EDs). We theoretically analyze the MSE performance of the proposed scheme and the convergence rate for a non-convex loss function. To improve the test accuracy of FEEL with the proposed scheme, we introduce the concept of adaptive absolute maximum (AAM). Our numerical results show that when the proposed scheme is used with AAM for FEEL, the test accuracy can reach up to 98% for heterogeneous data distribution.

Key facts. Cited by 9 papers in the map (data through 7 Oct 2026). Builds on 25 of its 49 references that are in the map. First posted on arXiv 13 Oct 2022.

Builds on

25 of 49 references.

References · 25

  1. 2016Communication-Efficient Learning of Deep Networks from Decentralized Datacited by 7,592
  2. 2018signSGD: Compressed Optimisation for Non-Convex Problemscited by 561
  3. 2019A Joint Learning and Communications Framework for Federated Learning over Wireless Networkscited by 262
  4. 2018Federated Learning via Over-the-Air Computationcited by 215
  5. 2018Broadband Analog Aggregation for Low-Latency Federated Edge Learning (Extended Version)cited by 186
  6. 2019Federated Learning over Wireless Fading Channelscited by 178
  7. 2021Distributed Learning in Wireless Networks: Recent Progress and Future Challengescited by 125
  8. 2020One-Bit Over-the-Air Aggregation for Communication-Efficient Federated Edge Learning: Design and Convergence Analysiscited by 108
  9. 2020Over-the-Air Federated Learning from Heterogeneous Datacited by 72
  10. 2020Ternary Compression for Communication-Efficient Federated Learningcited by 57
  11. 2020Blind Federated Edge Learningcited by 42
  12. 2019Over-the-Air Computation Systems: Optimization, Analysis and Scaling Lawscited by 25
13 more
  1. 20185G New Radio: Unveiling the Essentials of the Next Generation Wireless Access Technologycited by 16
  2. 2021Distributed Learning over a Wireless Network with FSK-Based Majority Votecited by 13
  3. 2022Random Orthogonalization for Federated Learning in Massive MIMO Systemscited by 13
  4. 2021On the Tradeoff between Energy, Precision, and Accuracy in Federated Quantized Neural Networkscited by 10
  5. 2019Collaborative Machine Learning at the Wireless Edge with Blind Transmitterscited by 9
  6. 2021Over-the-Air Computation with DFT-spread OFDM for Federated Edge Learningcited by 9
  7. 2020Over-the-Air Computation Systems: Optimal Design with Sum-Power Constraintcited by 8
  8. 2022Distributed Learning over a Wireless Network with Non-coherent Majority Vote Computationcited by 8
  9. 2020Cluster-Based Cooperative Digital Over-the-Air Aggregation for Wireless Federated Edge Learningcited by 6
  10. 2022A Demonstration of Over-the-Air Computation for Federated Edge Learningcited by 4
  11. 2022Over-the-Air Computation over Balanced Numeralscited by 4
  12. 2021Wyner-Ziv Gradient Compression for Federated Learningcited by 3
  13. 2022Chirp-Based Over-the-Air Computation for Long-Range Federated Edge Learningcited by 2

Built on by

Cited by year

Citing papers per year: 2022: 2; 2023: 2; 2024: 4; 2025: 1.

9 papers.

  1. 2022A Survey on Over-the-Air Computationcited by 19
  2. 2022Federated Learning and Meta Learning: Approaches, Applications, and Directionscited by 12
  3. 2024Training Machine Learning models at the Edge: A Surveycited by 11
  4. 2023Blind Federated Learning via Over-the-Air q-QAMcited by 7
  5. 2024Adaptive Federated Learning Over the Aircited by 4
  6. 2024Private Collaborative Edge Inference via Over-the-Air Computationcited by 4
  7. 2024Scalable and Reliable Over-the-Air Federated Edge Learningcited by 1
  8. 2025VecComp: Vector Computing via MIMO Digital Over-the-Air Computationcited by 1
  9. 2023Wireless Federated k-Means Clustering with Non-coherent Over-the-Air Computationcited by 0