mlsanityData to 7 Oct 2026

Compute-Update Federated Learning: A Lattice Coding Approach Over-the-Air

arXiv 2409.06343 ↗First posted 10 Sep 2024cs.IT cs.AI cs.LGcited by 5

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Abstract (arXiv)

This paper introduces a federated learning framework that enables over-the-air computation via digital communications, using a new joint source-channel coding scheme. Without relying on channel state information at devices, this scheme employs lattice codes to both quantize model parameters and exploit interference from the devices. We propose a novel receiver structure at the server, designed to reliably decode an integer combination of the quantized model parameters as a lattice point for the purpose of aggregation. We present a mathematical approach to derive a convergence bound for the proposed scheme and offer design remarks. In this context, we suggest an aggregation metric and a corresponding algorithm to determine effective integer coefficients for the aggregation in each communication round. Our results illustrate that, regardless of channel dynamics and data heterogeneity, our scheme consistently delivers superior learning accuracy across various parameters and markedly surpasses other over-the-air methodologies.

Key facts. Cited by 5 papers in the map (data through 7 Oct 2026). Builds on 22 of its 48 references that are in the map. First posted on arXiv 10 Sep 2024.

Builds on

22 of 48 references.

References · 22

  1. 2016Communication-Efficient Learning of Deep Networks from Decentralized Datacited by 7,592
  2. 2019Federated Learning: Challenges, Methods, and Future Directionscited by 1,531
  3. 2018signSGD: Compressed Optimisation for Non-Convex Problemscited by 561
  4. 2016Linear Convergence of Gradient and Proximal-Gradient Methods Under the Polyak-Lojasiewicz Conditioncited by 526
  5. 2018Federated Learning via Over-the-Air Computationcited by 215
  6. 2018Broadband Analog Aggregation for Low-Latency Federated Edge Learning (Extended Version)cited by 186
  7. 2019Federated Learning over Wireless Fading Channelscited by 178
  8. 2020One-Bit Over-the-Air Aggregation for Communication-Efficient Federated Edge Learning: Design and Convergence Analysiscited by 108
  9. 2020UVeQFed: Universal Vector Quantization for Federated Learningcited by 103
  10. 2020Communication-Efficient and Distributed Learning Over Wireless Networks: Principles and Applicationscited by 62
  11. 2019On Analog Gradient Descent Learning over Multiple Access Fading Channelscited by 61
  12. 2021Optimized Power Control Design for Over-the-Air Federated Edge Learningcited by 45
10 more
  1. 2020Blind Federated Edge Learningcited by 42
  2. 2020Federated Learning via Intelligent Reflecting Surfacecited by 38
  3. 2022Joint Privacy Enhancement and Quantization in Federated Learningcited by 34
  4. 2021Accelerated Gradient Descent Learning over Multiple Access Fading Channelscited by 14
  5. 2022Scalable Hierarchical Over-the-Air Federated Learningcited by 14
  6. 2024Hierarchical Over-the-Air Federated Learning with Awareness of Interference and Data Heterogeneitycited by 6
  7. 2024Federated Learning via Lattice Joint Source-Channel Codingcited by 6
  8. 2022Communication-Efficient Federated Learning over MIMO Multiple Access Channelscited by 4
  9. 2022Accelerating Distributed Optimization via Over-the-Air Computingcited by 4
  10. 2021Wireless Network Coding with Intelligent Reflecting Surfacescited by 3

Built on by

Cited by year (2026 to 7 Oct)

Citing papers per year: 2024: 1; 2025: 1; 2026: 3.

5 papers.

  1. 2024Over-the-Air Federated Learning via Weighted Aggregationcited by 5
  2. 2025Over-the-Air Federated Learning: Rethinking Edge AI Through Signal Processingcited by 3
  3. 2026Out-of-Air Computation: Enabling Structured Function Extraction from Wireless Superpositioncited by 0
  4. 2026AirPASS: Over-the-Air Federated Learning via Pinching Antenna Systemscited by 0
  5. 2026Progressive-Resolution Secure Aggregation for Federated Learningcited by 0