mlsanityData to 7 Oct 2026

Communicate to Learn at the Edge

arXiv 2009.13269 ↗First posted 28 Sep 2020eess.SP cs.IT cs.LGcited by 27

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Abstract (arXiv)

Bringing the success of modern machine learning (ML) techniques to mobile devices can enable many new services and businesses, but also poses significant technical and research challenges. Two factors that are critical for the success of ML algorithms are massive amounts of data and processing power, both of which are plentiful, yet highly distributed at the network edge. Moreover, edge devices are connected through bandwidth- and power-limited wireless links that suffer from noise, time-variations, and interference. Information and coding theory have laid the foundations of reliable and efficient communications in the presence of channel imperfections, whose application in modern wireless networks have been a tremendous success. However, there is a clear disconnect between the current coding and communication schemes, and the ML algorithms deployed at the network edge. In this paper, we challenge the current approach that treats these problems separately, and argue for a joint communication and learning paradigm for both the training and inference stages of edge learning.

Key facts. Cited by 27 papers in the map (data through 7 Oct 2026). First posted on arXiv 28 Sep 2020.

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Citing papers per year: 2021: 12; 2022: 9; 2023: 3; 2024: 3.

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  1. 2022Beyond Transmitting Bits: Context, Semantics, and Task-Oriented Communicationscited by 166
  2. 2021Distributed Learning in Wireless Networks: Recent Progress and Future Challengescited by 125
  3. 2021Dynamic Scheduling for Over-the-Air Federated Edge Learning with Energy Constraintscited by 34
  4. 2024Joint Source-Channel Coding: Fundamentals and Recent Progress in Practical Designscited by 32
  5. 2021Federated Edge Learning with Misaligned Over-The-Air Computationcited by 28
  6. 2021Time-Correlated Sparsification for Communication-Efficient Federated Learningcited by 25
  7. 2021Communication-Efficient Federated Learning via Quantized Compressed Sensingcited by 17
  8. 2022Semi-Decentralized Federated Learning with Collaborative Relayingcited by 16
  9. 2022Multi-user Co-inference with Batch Processing Capable Edge Servercited by 11
  10. 2024Mobility Accelerates Learning: Convergence Analysis on Hierarchical Federated Learning in Vehicular Networkscited by 11
  11. 2022Over-the-Air Federated Edge Learning with Hierarchical Clusteringcited by 10
  12. 2021Fast Federated Edge Learning with Overlapped Communication and Computation and Channel-Aware Fair Client Schedulingcited by 9
15 more
  1. 2022Over-the-Air Ensemble Inference with Model Privacycited by 9
  2. 2022Robust Federated Learning with Connectivity Failures: A Semi-Decentralized Framework with Collaborative Relayingcited by 5
  3. 2021Denoising Noisy Neural Networks: A Bayesian Approach with Compensationcited by 4
  4. 2021Collaborative Learning over Wireless Networks: An Introductory Overviewcited by 4
  5. 2022The Internet of Senses: Building on Semantic Communications and Edge Intelligencecited by 4
  6. 2024Private Collaborative Edge Inference via Over-the-Air Computationcited by 4
  7. 2022Remote Contextual Banditscited by 3
  8. 2023Adaptive Early Exiting for Collaborative Inference over Noisy Wireless Channelscited by 3
  9. 2023Graph Neural Networks over the Air for Decentralized Tasks in Wireless Networkscited by 2
  10. 2021Energy-Efficient Multi-Orchestrator Mobile Edge Learningcited by 1
  11. 2021Motivating Learners in Multi-Orchestrator Mobile Edge Learning: A Stackelberg Game Approachcited by 1
  12. 2022BSAC-CoEx: Coexistence of URLLC and Distributed Learning Services via Device Selectioncited by 1
  13. 2021Effective Communications: A Joint Learning and Communication Framework for Multi-Agent Reinforcement Learning over Noisy Channelscited by 0
  14. 2021AirNet: Neural Network Transmission over the Aircited by 0
  15. 2023Revolutionizing Wireless Networks with Federated Learning: A Comprehensive Reviewcited by 0