mlsanityData to 14 Oct 2026

Knowledge Distillation For Wireless Edge Learning

arXiv 2104.06374 ↗First posted 3 Apr 2021cs.NI cs.AI cs.LGcited by 1

On the map: Knowledge distillation → Federated, robust and private distillation

Abstract (arXiv)

In this paper, we propose a framework for predicting frame errors in the collaborative spectrally congested wireless environments of the DARPA Spectrum Collaboration Challenge (SC2) via a recently collected dataset. We employ distributed deep edge learning that is shared among edge nodes and a central cloud. Using this close-to-practice dataset, we find that widely used federated learning approaches, specially those that are privacy preserving, are worse than local training for a wide range of settings. We hence utilize the synthetic minority oversampling technique to maintain privacy via avoiding the transfer of local data to the cloud, and utilize knowledge distillation with an aim to benefit from high cloud computing and storage capabilities. The proposed framework achieves overall better performance than both local and federated training approaches, while being robust against catastrophic failures as well as challenging channel conditions that result in high frame error rates.

Key facts. Cited by 1 paper in the map (data through 14 Oct 2026). Placed in Federated, robust and private distillation, one of 12 directions in Knowledge distillation. Builds on 9 of its 16 references that are in the map. First posted on arXiv 3 Apr 2021.

Builds on

9 of 16 references.

References · 9

  1. 2015Distilling the Knowledge in a Neural Networkcited by 10,324
  2. 2016Communication-Efficient Learning of Deep Networks from Decentralized Datacited by 7,669
  3. 2016Deep Learning with Differential Privacycited by 2,728
  4. 2016Membership Inference Attacks against Machine Learning Modelscited by 2,281
  5. 2018Exploiting Unintended Feature Leakage in Collaborative Learningcited by 520
  6. 2019Agnostic Federated Learningcited by 456
  7. 2020Salvaging Federated Learning by Local Adaptationcited by 142
  8. 2020Enabling AI in Future Wireless Networks: A Data Life Cycle Perspectivecited by 14
  9. 2020Deep Learning for Frame Error Prediction using a DARPA Spectrum Collaboration Challenge (SC2) Datasetcited by 1

Built on by

Cited by year

Citing papers per year: 2024: 1.

1 paper.

  1. 2024Simulation-Enhanced Data Augmentation for Machine Learning Pathloss Predictioncited by 0

Similar, not cited

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  1. 2023Edge Intelligence Over the Air: Two Faces of Interference in Federated Learning
  2. 2025Communication-Efficient Federated Learning by Quantized Variance Reduction for Heterogeneous Wireless Edge Networks
  3. 2023Knowledge Distillation in Federated Edge Learning: A Survey
  4. 2022CFLIT: Coexisting Federated Learning and Information Transfer
  5. 2026Non-Coherent Over-the-Air Federated Learning: Protocol, Convergence, and Device Scheduling
  6. 2025Over-the-Air Federated Learning: Rethinking Edge AI Through Signal Processing
  7. 2024Providing Differential Privacy for Federated Learning Over Wireless: A Cross-layer Framework
  8. 2023Over-The-Air Federated Learning: Status Quo, Open Challenges, and Future Directions