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Wireless Federated Distillation for Distributed Edge Learning with Heterogeneous Data

arXiv 1907.02745 ↗First posted 5 Jul 2019cs.IT cs.LG math.ITcited by 39

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Abstract (arXiv)

Cooperative training methods for distributed machine learning typically assume noiseless and ideal communication channels. This work studies some of the opportunities and challenges arising from the presence of wireless communication links. We specifically consider wireless implementations of Federated Learning (FL) and Federated Distillation (FD), as well as of a novel Hybrid Federated Distillation (HFD) scheme. Both digital implementations based on separate source-channel coding and over-the-air computing implementations based on joint source-channel coding are proposed and evaluated over Gaussian multiple-access channels.

Key facts. Cited by 39 papers in the map (data through 7 Oct 2026). First posted on arXiv 5 Jul 2019.

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Citing papers per year: 2021: 11; 2022: 3; 2023: 9; 2024: 11; 2025: 2; 2026: 3.

39 papers.

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  1. 2021Distributed Learning in Wireless Networks: Recent Progress and Future Challengescited by 125
  2. 2021FedAUX: Leveraging Unlabeled Auxiliary Data in Federated Learningcited by 67
  3. 2023FedGH: Heterogeneous Federated Learning with Generalized Global Headercited by 56
  4. 2021Federated Learning over Wireless Device-to-Device Networks: Algorithms and Convergence Analysiscited by 51
  5. 2023pFedLoRA: Model-Heterogeneous Personalized Federated Learning with LoRA Tuningcited by 38
  6. 2024pFedMoE: Data-Level Personalization with Mixture of Experts for Model-Heterogeneous Personalized Federated Learningcited by 21
  7. 2024Federated Model Heterogeneous Matryoshka Representation Learningcited by 21
  8. 2021Architectural Patterns for the Design of Federated Learning Systemscited by 17
  9. 2021Decentralized and Model-Free Federated Learning: Consensus-Based Distillation in Function Spacecited by 17
  10. 2023HiFlash: Communication-Efficient Hierarchical Federated Learning with Adaptive Staleness Control and Heterogeneity-aware Client-Edge Associationcited by 17
  11. 2021Accelerated Gradient Descent Learning over Multiple Access Fading Channelscited by 14
  12. 2024Federated Distillation: A Surveycited by 14
27 more
  1. 2021Federated Learning over Wireless Networks: A Band-limited Coordinated Descent Approachcited by 11
  2. 2024Training Machine Learning models at the Edge: A Surveycited by 11
  3. 2023FedSSA: Semantic Similarity-based Aggregation for Efficient Model-Heterogeneous Personalized Federated Learningcited by 10
  4. 2021FLRA: A Reference Architecture for Federated Learning Systemscited by 9
  5. 2023pFedES: Model Heterogeneous Personalized Federated Learning with Feature Extractor Sharingcited by 9
  6. 2024Knowledge Distillation in Federated Learning: a Survey on Long Lasting Challenges and New Solutionscited by 8
  7. 2021Semi-asynchronous Hierarchical Federated Learning for Cooperative Intelligent Transportation Systemscited by 6
  8. 2022Federated Distillation based Indoor Localization for IoT Networkscited by 6
  9. 2023Knowledge Distillation in Federated Edge Learning: A Surveycited by 5
  10. 2023FedAL: Black-Box Federated Knowledge Distillation Enabled by Adversarial Learningcited by 5
  11. 2022Time Minimization in Hierarchical Federated Learningcited by 4
  12. 2021Over-the-Air Statistical Estimationcited by 3
  13. 2024pFedAFM: Adaptive Feature Mixture for Batch-Level Personalization in Heterogeneous Federated Learningcited by 3
  14. 2024Uncertainty Estimation in Multi-Agent Distributed Learning for AI-Enabled Edge Devicescited by 2
  15. 2025Federated Learning-Distillation Alternation for Resource-Constrained IoTcited by 2
  16. 2021Nothing Wasted: Full Contribution Enforcement in Federated Edge Learningcited by 1
  17. 2024Feature-based Federated Transfer Learning: Communication Efficiency, Robustness and Privacycited by 1
  18. 2024Edge AI Collaborative Learning: Bayesian Approaches to Uncertainty Estimationcited by 1
  19. 2024On the Impact of White-box Deployment Strategies for Edge AI on Latency and Model Performancecited by 1
  20. 2022Decision Models for Selecting Federated Learning Architecture Patternscited by 0
  21. 2023Joint Power Control and Data Size Selection for Over-the-Air Computation Aided Federated Learningcited by 0
  22. 2023Heterogeneous Federated Learning Using Knowledge Codistillationcited by 0
  23. 2024BEFL: Balancing Energy Consumption in Federated Learning for Mobile Edge IoTcited by 0
  24. 2025P2U: Progressive Precision Update For Efficient Model Distributioncited by 0
  25. 2026Hybrid Federated Learning for Noise-Robust Trainingcited by 0
  26. 2026Social Hippocampus Memory Learningcited by 0
  27. 2026Networking-Aware Energy Efficiency in Agentic AI Inference: A Surveycited by 0