# Cooperative Learning via Federated Distillation over Fading Channels

arXiv: https://arxiv.org/abs/2002.01337 · HTML: https://mlsanity.com/paper/2002.01337/

## Key facts

- Cited by 16 papers in the map (data through 13 Oct 2026).
- First posted on arXiv 3 Feb 2020.

## Built on by (most cited first, 16 of 16)

- [Distributed Learning in Wireless Networks: Recent Progress and Future Challenges](https://mlsanity.com/paper/2104.02151/) (2021)
- [FedGH: Heterogeneous Federated Learning with Generalized Global Header](https://mlsanity.com/paper/2303.13137/) (2023)
- [Federated Model Heterogeneous Matryoshka Representation Learning](https://mlsanity.com/paper/2406.00488/) (2024)
- [FedSSA: Semantic Similarity-based Aggregation for Efficient Model-Heterogeneous Personalized Federated Learning](https://mlsanity.com/paper/2312.09006/) (2023)
- [pFedES: Model Heterogeneous Personalized Federated Learning with Feature Extractor Sharing](https://mlsanity.com/paper/2311.06879/) (2023)
- [Federated Distillation based Indoor Localization for IoT Networks](https://mlsanity.com/paper/2205.11440/) (2022)
- [Federated Learning based Hierarchical 3D Indoor Localization](https://mlsanity.com/paper/2303.00450/) (2023)
- [Knowledge Distillation in Federated Edge Learning: A Survey](https://mlsanity.com/paper/2301.05849/) (2023)
- [FedPDD: A Privacy-preserving Double Distillation Framework for Cross-silo Federated Recommendation](https://mlsanity.com/paper/2305.06272/) (2023)
- [Multiple Access in the Era of Distributed Computing and Edge Intelligence](https://mlsanity.com/paper/2403.07903/) (2024)
- [Federated Learning with Downlink Device Selection](https://mlsanity.com/paper/2107.03510/) (2021)
- [Adaptive Scheduling for Machine Learning Tasks over Networks](https://mlsanity.com/paper/2101.10007/) (2021)
- [Linear Regression over Networks with Communication Guarantees](https://mlsanity.com/paper/2103.04140/) (2021)
- [Optimal Transceiver Design in Over-the-Air Federated Distillation](https://mlsanity.com/paper/2507.15256/) (2025)
- [Heterogeneous Federated Learning Using Knowledge Codistillation](https://mlsanity.com/paper/2310.02549/) (2023)
- [Social Hippocampus Memory Learning](https://mlsanity.com/paper/2603.25614/) (2026)

## Abstract (arXiv)

Cooperative training methods for distributed machine learning are typically based on the exchange of local gradients or local model parameters. The latter approach is known as Federated Learning (FL). An alternative solution with reduced communication overhead, referred to as Federated Distillation (FD), was recently proposed that exchanges only averaged model outputs. While prior work studied implementations of FL over wireless fading channels, here we propose wireless protocols for FD and for an enhanced version thereof that leverages an offline communication phase to communicate ``mixed-up'' covariate vectors. The proposed implementations consist of different combinations of digital schemes based on separate source-channel coding and of over-the-air computing strategies based on analog joint source-channel coding. It is shown that the enhanced version FD has the potential to significantly outperform FL in the presence of limited spectral resources.