# Scalable federated machine learning with FEDn

arXiv: https://arxiv.org/abs/2103.00148 · HTML: https://mlsanity.com/paper/2103.00148/

## Key facts

- Cited by 16 papers in the map (data through 14 Oct 2026).
- Builds on 24 of its 45 references that are in the map.
- First posted on arXiv 27 Feb 2021.

## Builds on (most cited first, 24 of 24)

- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [Communication-Efficient Learning of Deep Networks from Decentralized Data](https://mlsanity.com/paper/1602.05629/) (2016)
- [Distributed Representations of Words and Phrases and their Compositionality](https://mlsanity.com/paper/1310.4546/) (2013)
- [Advances and Open Problems in Federated Learning](https://mlsanity.com/paper/1912.04977/) (2019)
- [Deep Learning with Differential Privacy](https://mlsanity.com/paper/1607.00133/) (2016)
- [Deep contextualized word representations](https://mlsanity.com/paper/1802.05365/) (2018)
- [Enriching Word Vectors with Subword Information](https://mlsanity.com/paper/1607.04606/) (2016)
- [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)
- [Towards Federated Learning at Scale: System Design](https://mlsanity.com/paper/1902.01046/) (2019)
- [LEAF: A Benchmark for Federated Settings](https://mlsanity.com/paper/1812.01097/) (2018)
- [Flower: A Friendly Federated Learning Research Framework](https://mlsanity.com/paper/2007.14390/) (2020)
- [Federated Machine Learning: Concept and Applications](https://mlsanity.com/paper/1902.04885/) (2019)
- [Federated Learning in Mobile Edge Networks: A Comprehensive Survey](https://mlsanity.com/paper/1909.11875/) (2019)
- [Don't Decay the Learning Rate, Increase the Batch Size](https://mlsanity.com/paper/1711.00489/) (2017)
- [FedML: A Research Library and Benchmark for Federated Machine Learning](https://mlsanity.com/paper/2007.13518/) (2020)
- [SecureBoost: A Lossless Federated Learning Framework](https://mlsanity.com/paper/1901.08755/) (2019)
- [Multi-Institutional Deep Learning Modeling Without Sharing Patient Data: A Feasibility Study on Brain Tumor Segmentation](https://mlsanity.com/paper/1810.04304/) (2018)
- [A generic framework for privacy preserving deep learning](https://mlsanity.com/paper/1811.04017/) (2018)
- [TiFL: A Tier-based Federated Learning System](https://mlsanity.com/paper/2001.09249/) (2020)
- [Privacy Preserving Vertical Federated Learning for Tree-based Models](https://mlsanity.com/paper/2008.06170/) (2020)
- [Astraea: Self-balancing Federated Learning for Improving Classification Accuracy of Mobile Deep Learning Applications](https://mlsanity.com/paper/1907.01132/) (2019)
- [Strategies and Principles of Distributed Machine Learning on Big Data](https://mlsanity.com/paper/1512.09295/) (2015)
- [Revocable Federated Learning: A Benchmark of Federated Forest](https://mlsanity.com/paper/1911.03242/) (2019)

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

- [Accelerating Fair Federated Learning: Adaptive Federated Adam](https://mlsanity.com/paper/2301.09357/) (2023)
- [pfl-research: simulation framework for accelerating research in Private Federated Learning](https://mlsanity.com/paper/2404.06430/) (2024)
- [FOCUS: Fairness via Agent-Awareness for Federated Learning on Heterogeneous Data](https://mlsanity.com/paper/2207.10265/) (2022)
- [FedQAS: Privacy-aware machine reading comprehension with federated learning](https://mlsanity.com/paper/2202.04742/) (2022)
- [Towards cost-effective and resource-aware aggregation at Edge for Federated Learning](https://mlsanity.com/paper/2204.07767/) (2022)
- [FedCSD: A Federated Learning Based Approach for Code-Smell Detection](https://mlsanity.com/paper/2306.00038/) (2023)
- [Federated Learning: A Cutting-Edge Survey of the Latest Advancements and Applications](https://mlsanity.com/paper/2310.05269/) (2023)
- [FedBot: Enhancing Privacy in Chatbots with Federated Learning](https://mlsanity.com/paper/2304.03228/) (2023)
- [Practical Feasibility of Gradient Inversion Attacks in Federated Learning](https://mlsanity.com/paper/2508.19819/) (2025)
- [Collaborative Distributed Machine Learning](https://mlsanity.com/paper/2309.16584/) (2023)
- [A Comprehensive Empirical Study of Bugs in Open-Source Federated Learning Frameworks](https://mlsanity.com/paper/2308.05014/) (2023)
- [Toward efficient resource utilization at edge nodes in federated learning](https://mlsanity.com/paper/2309.10367/) (2023)
- [Federated Computing -- Survey on Building Blocks, Extensions and Systems](https://mlsanity.com/paper/2404.02779/) (2024)
- [Personalized Multi-tier Federated Learning](https://mlsanity.com/paper/2407.14251/) (2024)
- [Scalability and Maintainability Challenges and Solutions in Machine Learning: Systematic Literature Review](https://mlsanity.com/paper/2504.11079/) (2025)
- [Making Cross-Continental Federated Learning Repeatable with FLIP: a Multi-Application Study](https://mlsanity.com/paper/2609.36001/) (2026)

## Abstract (arXiv)

Federated machine learning has great promise to overcome the input privacy challenge in machine learning. The appearance of several projects capable of simulating federated learning has led to a corresponding rapid progress on algorithmic aspects of the problem. However, there is still a lack of federated machine learning frameworks that focus on fundamental aspects such as scalability, robustness, security, and performance in a geographically distributed setting. To bridge this gap we have designed and developed the FEDn framework. A main feature of FEDn is to support both cross-device and cross-silo training settings. This makes FEDn a powerful tool for researching a wide range of machine learning applications in a realistic setting.