mlsanityData to 7 Oct 2026

Federated Learning via Lattice Joint Source-Channel Coding

arXiv 2403.01023 ↗First posted 1 Mar 2024cs.IT cs.LG math.ITcited by 6

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Abstract (arXiv)

This paper introduces a universal 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. A novel two-layer receiver structure at the server is designed to reliably decode an integer combination of the quantized model parameters as a lattice point for the purpose of aggregation. Numerical experiments validate the effectiveness of the proposed scheme. Even with the challenges posed by channel conditions and device heterogeneity, the proposed scheme markedly surpasses other over-the-air FL strategies.

Key facts. Cited by 6 papers in the map (data through 7 Oct 2026). Builds on 14 of its 25 references that are in the map. First posted on arXiv 1 Mar 2024.

Builds on

14 of 25 references.

References · 14

  1. 2016Communication-Efficient Learning of Deep Networks from Decentralized Datacited by 7,592
  2. 2018signSGD: Compressed Optimisation for Non-Convex Problemscited by 561
  3. 2018Federated Learning via Over-the-Air Computationcited by 215
  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. 2020One-Bit Over-the-Air Aggregation for Communication-Efficient Federated Edge Learning: Design and Convergence Analysiscited by 108
  7. 2020UVeQFed: Universal Vector Quantization for Federated Learningcited by 103
  8. 2019On Analog Gradient Descent Learning over Multiple Access Fading Channelscited by 61
  9. 2021Optimized Power Control Design for Over-the-Air Federated Edge Learningcited by 45
  10. 2020Blind Federated Edge Learningcited by 42
  11. 2022Joint Privacy Enhancement and Quantization in Federated Learningcited by 34
  12. 2021Accelerated Gradient Descent Learning over Multiple Access Fading Channelscited by 14
2 more
  1. 2022Scalable Hierarchical Over-the-Air Federated Learningcited by 14
  2. 2021Wireless Network Coding with Intelligent Reflecting Surfacescited by 3

Built on by

Cited by year (2026 to 7 Oct)

Citing papers per year: 2024: 2; 2025: 2; 2026: 2.

6 papers.

  1. 2024Compute-Update Federated Learning: A Lattice Coding Approach Over-the-Aircited by 5
  2. 2024Over-the-Air Federated Learning via Weighted Aggregationcited by 5
  3. 2025Satellite Edge Artificial Intelligence with Large Models: Architectures and Technologiescited by 4
  4. 2025Over-the-Air Federated Learning: Rethinking Edge AI Through Signal Processingcited by 3
  5. 2026Out-of-Air Computation: Enabling Structured Function Extraction from Wireless Superpositioncited by 0
  6. 2026Progressive-Resolution Secure Aggregation for Federated Learningcited by 0