# Knowledge Distillation for Federated Learning: a Practical Guide

arXiv: https://arxiv.org/abs/2211.04742 · HTML: https://mlsanity.com/paper/2211.04742/

## Key facts

- Cited by 31 papers in the map (data through 14 Oct 2026).
- Placed in Federated, robust and private distillation, one of 12 directions in Knowledge distillation.
- Builds on 50 of its 79 references that are in the map.
- First posted on arXiv 9 Nov 2022.

## Placed in

- Knowledge distillation / [Federated, robust and private distillation](https://mlsanity.com/distill/federated-robust-and-private-distillation/)

## Builds on (most cited first, 30 of 50)

- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [Distilling the Knowledge in a Neural Network](https://mlsanity.com/paper/1503.02531/) (2015)
- [Communication-Efficient Learning of Deep Networks from Decentralized Data](https://mlsanity.com/paper/1602.05629/) (2016)
- [Grad-CAM: Visual Explanations from Deep Networks via Gradient-based Localization](https://mlsanity.com/paper/1610.02391/) (2016)
- [Federated Optimization in Heterogeneous Networks](https://mlsanity.com/paper/1812.06127/) (2018)
- [Knowledge Distillation: A Survey](https://mlsanity.com/paper/2006.05525/) (2020)
- [Federated Learning: Strategies for Improving Communication Efficiency](https://mlsanity.com/paper/1610.05492/) (2016)
- [On the Convergence of FedAvg on Non-IID Data](https://mlsanity.com/paper/1907.02189/) (2019)
- [Federated Learning with Non-IID Data](https://mlsanity.com/paper/1806.00582/) (2018)
- [Adaptive Federated Optimization](https://mlsanity.com/paper/2003.00295/) (2020)
- [An Empirical Investigation of Catastrophic Forgetting in Gradient-Based Neural Networks](https://mlsanity.com/paper/1312.6211/) (2013)
- [Ensemble Distillation for Robust Model Fusion in Federated Learning](https://mlsanity.com/paper/2006.07242/) (2020)
- [Federated Learning on Non-IID Data Silos: An Experimental Study](https://mlsanity.com/paper/2102.02079/) (2021)
- [Federated Learning Based on Dynamic Regularization](https://mlsanity.com/paper/2111.04263/) (2021)
- [Deep Gradient Compression: Reducing the Communication Bandwidth for Distributed Training](https://mlsanity.com/paper/1712.01887/) (2017)
- [FedMD: Heterogenous Federated Learning via Model Distillation](https://mlsanity.com/paper/1910.03581/) (2019)
- [Robust and Communication-Efficient Federated Learning from Non-IID Data](https://mlsanity.com/paper/1903.02891/) (2019)
- [Data-Free Knowledge Distillation for Heterogeneous Federated Learning](https://mlsanity.com/paper/2105.10056/) (2021)
- [FedPAQ: A Communication-Efficient Federated Learning Method with Periodic Averaging and Quantization](https://mlsanity.com/paper/1909.13014/) (2019)
- [Semi-supervised Knowledge Transfer for Deep Learning from Private Training Data](https://mlsanity.com/paper/1610.05755/) (2016)
- [A Field Guide to Federated Optimization](https://mlsanity.com/paper/2107.06917/) (2021)
- [Communication-Efficient On-Device Machine Learning: Federated Distillation and Augmentation under Non-IID Private Data](https://mlsanity.com/paper/1811.11479/) (2018)
- [No Fear of Heterogeneity: Classifier Calibration for Federated Learning with Non-IID Data](https://mlsanity.com/paper/2106.05001/) (2021)
- [FedBE: Making Bayesian Model Ensemble Applicable to Federated Learning](https://mlsanity.com/paper/2009.01974/) (2020)
- [SCAFFOLD: Stochastic Controlled Averaging for Federated Learning](https://mlsanity.com/paper/1910.06378/) (2019)
- [Fine-tuning Global Model via Data-Free Knowledge Distillation for Non-IID Federated Learning](https://mlsanity.com/paper/2203.09249/) (2022)
- [Large scale distributed neural network training through online distillation](https://mlsanity.com/paper/1804.03235/) (2018)
- [Distillation-Based Semi-Supervised Federated Learning for Communication-Efficient Collaborative Training with Non-IID Private Data](https://mlsanity.com/paper/2008.06180/) (2020)
- [Overcoming Forgetting in Federated Learning on Non-IID Data](https://mlsanity.com/paper/1910.07796/) (2019)
- [Local Learning Matters: Rethinking Data Heterogeneity in Federated Learning](https://mlsanity.com/paper/2111.14213/) (2021)

## Built on by (most cited first, 30 of 31)

- [Federated Unlearning: A Survey on Methods, Design Guidelines, and Evaluation Metrics](https://mlsanity.com/paper/2401.05146/) (2024)
- [A Survey of What to Share in Federated Learning: Perspectives on Model Utility, Privacy Leakage, and Communication Efficiency](https://mlsanity.com/paper/2307.10655/) (2023)
- [Federated Distillation: A Survey](https://mlsanity.com/paper/2404.08564/) (2024)
- [Knowledge Distillation in Federated Learning: a Survey on Long Lasting Challenges and New Solutions](https://mlsanity.com/paper/2406.10861/) (2024)
- [Real World Federated Learning with a Knowledge Distilled Transformer for Cardiac CT Imaging](https://mlsanity.com/paper/2407.07557/) (2024)
- [Continual Adaptation of Vision Transformers for Federated Learning](https://mlsanity.com/paper/2306.09970/) (2023)
- [Adapter-based Selective Knowledge Distillation for Federated Multi-domain Meeting Summarization](https://mlsanity.com/paper/2308.03275/) (2023)
- [One-shot Federated Learning Methods: A Practical Guide](https://mlsanity.com/paper/2502.09104/) (2025)
- [Knowledge Distillation in Federated Edge Learning: A Survey](https://mlsanity.com/paper/2301.05849/) (2023)
- [Towards Diverse Device Heterogeneous Federated Learning via Task Arithmetic Knowledge Integration](https://mlsanity.com/paper/2409.18461/) (2024)
- [Federated Learning for 6G: Paradigms, Taxonomy, Recent Advances and Insights](https://mlsanity.com/paper/2312.04688/) (2023)
- [Federated Class-Incremental Learning with New-Class Augmented Self-Distillation](https://mlsanity.com/paper/2401.00622/) (2024)
- [Logits Poisoning Attack in Federated Distillation](https://mlsanity.com/paper/2401.03685/) (2024)
- [FedQUIT: On-Device Federated Unlearning via a Quasi-Competent Virtual Teacher](https://mlsanity.com/paper/2408.07587/) (2024)
- [FedPromo: Federated Lightweight Proxy Models at the Edge Bring New Domains to Foundation Models](https://mlsanity.com/paper/2508.03356/) (2025)
- [Peak-Controlled Logits Poisoning Attack in Federated Distillation](https://mlsanity.com/paper/2407.18039/) (2024)
- [Federated Unlearning Made Practical: Seamless Integration via Negated Pseudo-Gradients](https://mlsanity.com/paper/2504.05822/) (2025)
- [Benchmarking Federated Learning for Throughput Prediction in 5G Live Streaming Applications](https://mlsanity.com/paper/2508.08479/) (2025)
- [CRAD: Class-wise Reliability-Aware Distillation for Decentralized Heterogeneous Federated Learning](https://mlsanity.com/paper/2609.00446/) (2026)
- [Heterogeneous Federated Learning Using Knowledge Codistillation](https://mlsanity.com/paper/2310.02549/) (2023)
- [CoDream: Exchanging dreams instead of models for federated aggregation with heterogeneous models](https://mlsanity.com/paper/2402.15968/) (2024)
- [Federated Learning for Collaborative Inference Systems: The Case of Early Exit Networks](https://mlsanity.com/paper/2405.04249/) (2024)
- [Federated Knowledge Transfer Fine-tuning Large Server Model with Resource-Constrained IoT Clients](https://mlsanity.com/paper/2407.05268/) (2024)
- [Applied Federated Model Personalisation in the Industrial Domain: A Comparative Study](https://mlsanity.com/paper/2409.06904/) (2024)
- [Asynchronous Federated Continual Segmentation with Evolving Clients and Label Spaces](https://mlsanity.com/paper/2503.15414/) (2025)
- [FedL2T: Personalized Federated Learning with Two-Teacher Distillation for Seizure Prediction](https://mlsanity.com/paper/2510.08984/) (2025)
- [SAM-Fed: SAM-Guided Federated Semi-Supervised Learning for Medical Image Segmentation](https://mlsanity.com/paper/2511.14302/) (2025)
- [SCENE OTA-FD: Self-Centering Noncoherent Estimator for Over-the-Air Federated Distillation](https://mlsanity.com/paper/2602.15326/) (2026)
- [Powerful Teachers Matter: Text-Guided Multi-view Knowledge Distillation with Visual Prior Enhancement](https://mlsanity.com/paper/2603.24208/) (2026)
- [Function-Space ADMM for Decentralized Federated Learning: A Control Theoretic Perspective](https://mlsanity.com/paper/2605.09356/) (2026)

## Abstract (arXiv)

Federated Learning (FL) enables the training of Deep Learning models without centrally collecting possibly sensitive raw data. The most used algorithms for FL are parameter-averaging based schemes (e.g., Federated Averaging) that, however, have well known limits, i.e., model homogeneity, high communication cost, poor performance in presence of heterogeneous data distributions. Federated adaptations of regular Knowledge Distillation (KD) can solve or mitigate the weaknesses of parameter-averaging FL algorithms while possibly introducing other trade-offs. In this article, we originally present a focused review of the state-of-the-art KD-based algorithms specifically tailored for FL, by providing both a novel classification of the existing approaches and a detailed technical description of their pros, cons, and tradeoffs.