mlsanityData to 14 Oct 2026

Learned Digital Codes for Over-the-Air Computation in Federated Edge Learning

arXiv 2512.19777 ↗First posted 22 Dec 2025cs.IT cs.AI cs.DCcited by 0

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Abstract (arXiv)

Federated edge learning (FEEL) enables wireless devices to collaboratively train a centralised model without sharing raw data, but repeated uplink transmission of model updates makes communication the dominant bottleneck. Over-the-air (OTA) aggregation alleviates this by exploiting the superposition property of the wireless channel, enabling simultaneous transmission and merging communication with computation. Digital OTA schemes extend this principle by incorporating the robustness of conventional digital communication, but current designs remain limited in low signal-to-noise ratio (SNR) regimes. This work proposes a learned digital OTA framework that improves recovery accuracy, convergence behaviour, and robustness to challenging SNR conditions while maintaining the same uplink overhead as state-of-the-art methods. The design integrates an unsourced random access (URA) codebook with vector quantisation and AMP-DA-Net, an unrolled approximate message passing (AMP)-style decoder trained end-to-end with the digital codebook and parameter server local training statistics. The proposed design extends OTA aggregation beyond averaging to a broad class of symmetric functions, including trimmed means and majority-based rules. Experiments on highly heterogeneous device datasets and varying numbers of active devices show that the proposed design extends reliable digital OTA operation by more than 10 dB into low SNR regimes while matching or improving performance across the full SNR range. The learned decoder remains effective under message corruption and nonlinear aggregation, highlighting the broader potential of end-to-end learned design for digital OTA communication in FEEL.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 25 of its 41 references that are in the map. First posted on arXiv 22 Dec 2025.

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25 of 41 references.

References · 25

  1. 2014Very Deep Convolutional Networks for Large-Scale Image Recognitioncited by 15,364
  2. 2016Communication-Efficient Learning of Deep Networks from Decentralized Datacited by 7,669
  3. 2018Federated Optimization in Heterogeneous Networkscited by 2,987
  4. 2016Federated Learning: Strategies for Improving Communication Efficiencycited by 1,475
  5. 2020Adaptive Federated Optimizationcited by 1,046
  6. 2019Towards Federated Learning at Scale: System Designcited by 832
  7. 2018signSGD: Compressed Optimisation for Non-Convex Problemscited by 571
  8. 2017TernGrad: Ternary Gradients to Reduce Communication in Distributed Deep Learningcited by 256
  9. 2017Sparse Communication for Distributed Gradient Descentcited by 249
  10. 2018Federated Learning via Over-the-Air Computationcited by 219
  11. 2018Broadband Analog Aggregation for Low-Latency Federated Edge Learning (Extended Version)cited by 191
  12. 2019Federated Learning over Wireless Fading Channelscited by 183
13 more
  1. 2021Distributed Learning in Wireless Networks: Recent Progress and Future Challengescited by 129
  2. 2020One-Bit Over-the-Air Aggregation for Communication-Efficient Federated Edge Learning: Design and Convergence Analysiscited by 112
  3. 2024Addressing Representation Collapse in Vector Quantized Models with One Linear Layercited by 80
  4. 2020AMP-Net: Denoising based Deep Unfolding for Compressive Image Sensingcited by 68
  5. 2021Dynamic Scheduling for Over-the-Air Federated Edge Learning with Energy Constraintscited by 36
  6. 2022Sparse Random Networks for Communication-Efficient Federated Learningcited by 32
  7. 2021Time-Correlated Sparsification for Communication-Efficient Federated Learningcited by 26
  8. 2021Distributed Learning over a Wireless Network with FSK-Based Majority Votecited by 13
  9. 2024Massive Digital Over-the-Air Computation for Communication-Efficient Federated Edge Learningcited by 13
  10. 2023ChannelComp: A General Method for Computation by Communicationscited by 10
  11. 2023Joint Design of Coding and Modulation for Digital Over-the-Air Computationcited by 4
  12. 2024Digital Over-the-Air Computation: Achieving High Reliability via Bit-Slicingcited by 4
  13. 2025On the Feasibility of Distributed Phase Synchronization for Coherent Signal Superpositioncited by 3

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