mlsanityData to 14 Oct 2026

Learned Digital Over-the-Air Computing for Federated Edge Learning

arXiv 2509.16577 ↗First posted 20 Sep 2025cs.LG eess.SPcited by 0

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Abstract (arXiv)

Over-the-air (OTA) aggregation enables federated edge learning (FEEL) by exploiting the superposition property of the wireless channel to merge communication with computation, eliminating the need to schedule and decode devices individually. Analog OTA schemes transmit uncoded updates but are sensitive to noise, fading, and power misalignment, motivating more robust digital alternatives. However, state-of-the-art (SoTA) digital OTA designs that combine unsourced random access (URA) with compressed sensing struggle in the low signal-to-noise ratio (SNR) regimes common in Internet of Things (IoT) deployments, where symbol recovery and active-device estimation become unreliable. We propose a learned digital OTA framework that jointly trains a URA codebook with an unrolled approximate message passing (AMP)-based decoder. The learned decoder incorporates per-layer damping, residual scaling, temperature-controlled Bayesian denoising, and a lightweight convolutional neural network (CNN) refinement, while the codebook is optimised end-to-end through a factorised parameterisation. At near-perfect-aggregation accuracy, the proposed design extends the viable SNR range by approximately 7,dB over the SoTA baseline at the same uplink overhead, and generalises across models, activity levels, and heterogeneous data.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 13 of its 22 references that are in the map. First posted on arXiv 20 Sep 2025.

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13 of 22 references.

References · 13

  1. 2016Communication-Efficient Learning of Deep Networks from Decentralized Datacited by 7,669
  2. 2016Federated Learning: Strategies for Improving Communication Efficiencycited by 1,475
  3. 2017Sparse Communication for Distributed Gradient Descentcited by 249
  4. 2019Federated Learning over Wireless Fading Channelscited by 183
  5. 2021Distributed Learning in Wireless Networks: Recent Progress and Future Challengescited by 129
  6. 2020One-Bit Over-the-Air Aggregation for Communication-Efficient Federated Edge Learning: Design and Convergence Analysiscited by 112
  7. 2024Addressing Representation Collapse in Vector Quantized Models with One Linear Layercited by 80
  8. 2020AMP-Net: Denoising based Deep Unfolding for Compressive Image Sensingcited by 68
  9. 2021Dynamic Scheduling for Over-the-Air Federated Edge Learning with Energy Constraintscited by 36
  10. 2021Distributed Learning over a Wireless Network with FSK-Based Majority Votecited by 13
  11. 2024Massive Digital Over-the-Air Computation for Communication-Efficient Federated Edge Learningcited by 13
  12. 2023OFDMA-F2L: Federated Learning With Flexible Aggregation Over an OFDMA Air Interfacecited by 6
1 more
  1. 2025On the Feasibility of Distributed Phase Synchronization for Coherent Signal Superpositioncited by 3

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  1. 2025Learned Digital Codes for Over-the-Air Computation in Federated Edge Learning
  2. 2025Over-the-Air Federated Learning: Rethinking Edge AI Through Signal Processing
  3. 2022Over-the-Air Federated Learning with Joint Adaptive Computation and Power Control
  4. 2023Edge Intelligence Over the Air: Two Faces of Interference in Federated Learning
  5. 2023Unsourced Massive Access-Based Digital Over-the-Air Computation for Efficient Federated Edge Learning
  6. 2023Over-The-Air Federated Learning: Status Quo, Open Challenges, and Future Directions
  7. 2025Noise Resilient Over-The-Air Federated Learning In Heterogeneous Wireless Networks
  8. 2026Non-Coherent Over-the-Air Federated Learning: Protocol, Convergence, and Device Scheduling