# Wireless Federated Distillation for Distributed Edge Learning with Heterogeneous Data

arXiv: https://arxiv.org/abs/1907.02745 · HTML: https://mlsanity.com/paper/1907.02745/

## Key facts

- Cited by 39 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 5 Jul 2019.

## Built on by (most cited first, 30 of 39)

- [Distributed Learning in Wireless Networks: Recent Progress and Future Challenges](https://mlsanity.com/paper/2104.02151/) (2021)
- [FedAUX: Leveraging Unlabeled Auxiliary Data in Federated Learning](https://mlsanity.com/paper/2102.02514/) (2021)
- [FedGH: Heterogeneous Federated Learning with Generalized Global Header](https://mlsanity.com/paper/2303.13137/) (2023)
- [Federated Learning over Wireless Device-to-Device Networks: Algorithms and Convergence Analysis](https://mlsanity.com/paper/2101.12704/) (2021)
- [pFedLoRA: Model-Heterogeneous Personalized Federated Learning with LoRA Tuning](https://mlsanity.com/paper/2310.13283/) (2023)
- [pFedMoE: Data-Level Personalization with Mixture of Experts for Model-Heterogeneous Personalized Federated Learning](https://mlsanity.com/paper/2402.01350/) (2024)
- [Federated Model Heterogeneous Matryoshka Representation Learning](https://mlsanity.com/paper/2406.00488/) (2024)
- [Architectural Patterns for the Design of Federated Learning Systems](https://mlsanity.com/paper/2101.02373/) (2021)
- [Decentralized and Model-Free Federated Learning: Consensus-Based Distillation in Function Space](https://mlsanity.com/paper/2104.00352/) (2021)
- [HiFlash: Communication-Efficient Hierarchical Federated Learning with Adaptive Staleness Control and Heterogeneity-aware Client-Edge Association](https://mlsanity.com/paper/2301.06447/) (2023)
- [Federated Distillation: A Survey](https://mlsanity.com/paper/2404.08564/) (2024)
- [Accelerated Gradient Descent Learning over Multiple Access Fading Channels](https://mlsanity.com/paper/2107.12452/) (2021)
- [Federated Learning over Wireless Networks: A Band-limited Coordinated Descent Approach](https://mlsanity.com/paper/2102.07972/) (2021)
- [Training Machine Learning models at the Edge: A Survey](https://mlsanity.com/paper/2403.02619/) (2024)
- [FedSSA: Semantic Similarity-based Aggregation for Efficient Model-Heterogeneous Personalized Federated Learning](https://mlsanity.com/paper/2312.09006/) (2023)
- [FLRA: A Reference Architecture for Federated Learning Systems](https://mlsanity.com/paper/2106.11570/) (2021)
- [pFedES: Model Heterogeneous Personalized Federated Learning with Feature Extractor Sharing](https://mlsanity.com/paper/2311.06879/) (2023)
- [Knowledge Distillation in Federated Learning: a Survey on Long Lasting Challenges and New Solutions](https://mlsanity.com/paper/2406.10861/) (2024)
- [Semi-asynchronous Hierarchical Federated Learning for Cooperative Intelligent Transportation Systems](https://mlsanity.com/paper/2110.09073/) (2021)
- [Federated Distillation based Indoor Localization for IoT Networks](https://mlsanity.com/paper/2205.11440/) (2022)
- [Knowledge Distillation in Federated Edge Learning: A Survey](https://mlsanity.com/paper/2301.05849/) (2023)
- [FedAL: Black-Box Federated Knowledge Distillation Enabled by Adversarial Learning](https://mlsanity.com/paper/2311.16584/) (2023)
- [Time Minimization in Hierarchical Federated Learning](https://mlsanity.com/paper/2210.04689/) (2022)
- [Over-the-Air Statistical Estimation](https://mlsanity.com/paper/2103.04014/) (2021)
- [pFedAFM: Adaptive Feature Mixture for Batch-Level Personalization in Heterogeneous Federated Learning](https://mlsanity.com/paper/2404.17847/) (2024)
- [Uncertainty Estimation in Multi-Agent Distributed Learning for AI-Enabled Edge Devices](https://mlsanity.com/paper/2403.09141/) (2024)
- [Federated Learning-Distillation Alternation for Resource-Constrained IoT](https://mlsanity.com/paper/2505.20456/) (2025)
- [Nothing Wasted: Full Contribution Enforcement in Federated Edge Learning](https://mlsanity.com/paper/2110.08330/) (2021)
- [Feature-based Federated Transfer Learning: Communication Efficiency, Robustness and Privacy](https://mlsanity.com/paper/2405.09014/) (2024)
- [Edge AI Collaborative Learning: Bayesian Approaches to Uncertainty Estimation](https://mlsanity.com/paper/2410.08651/) (2024)

## 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.