mlsanityData to 7 Oct 2026

Over-the-Air Computation over Balanced Numerals

arXiv 2209.11004 ↗First posted 22 Sep 2022eess.SP cs.AI cs.ITcited by 4

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Abstract (arXiv)

In this study, a digital over-the-air computation (OAC) scheme for achieving continuous-valued gradient aggregation is proposed. It is shown 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 using this property, the proposed scheme encodes the local gradients into a set of numerals. It then determines the positions of the activated orthogonal frequency division multiplexing (OFDM) subcarriers by using the values of the numerals. To eliminate the need for a precise sample-level time synchronization, channel estimation overhead, and power instabilities due to the 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). Finally, the theoretical mean squared error (MSE) performance of the proposed scheme is derived and its performance for federated edge learning (FEEL) is demonstrated.

Key facts. Cited by 4 papers in the map (data through 7 Oct 2026). Builds on 11 of its 21 references that are in the map. First posted on arXiv 22 Sep 2022.

Builds on

11 of 21 references.

References · 11

  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. 2018Broadband Analog Aggregation for Low-Latency Federated Edge Learning (Extended Version)cited by 186
  5. 2019Federated Learning over Wireless Fading Channelscited by 178
  6. 2021Distributed Learning in Wireless Networks: Recent Progress and Future Challengescited by 125
  7. 2020One-Bit Over-the-Air Aggregation for Communication-Efficient Federated Edge Learning: Design and Convergence Analysiscited by 108
  8. 2020Over-the-Air Federated Learning from Heterogeneous Datacited by 72
  9. 2019Over-the-Air Computation Systems: Optimization, Analysis and Scaling Lawscited by 25
  10. 2021Distributed Learning over a Wireless Network with FSK-Based Majority Votecited by 13
  11. 2021Over-the-Air Computation with DFT-spread OFDM for Federated Edge Learningcited by 9

Built on by

Cited by year

Citing papers per year: 2022: 2; 2023: 1; 2024: 0; 2025: 1.

4 papers.

  1. 2022A Survey on Over-the-Air Computationcited by 19
  2. 2022Over-the-Air Computation Based on Balanced Number Systems for Federated Edge Learningcited by 9
  3. 2023Graph Neural Networks over the Air for Decentralized Tasks in Wireless Networkscited by 2
  4. 2025OptiVote: Non-Coherent FSO Over-the-Air Majority Vote for Communication-Efficient Distributed Federated Learning in Space Data Centerscited by 0