# On-device Federated Learning with Flower

arXiv: https://arxiv.org/abs/2104.03042 · HTML: https://mlsanity.com/paper/2104.03042/

## Key facts

- Cited by 13 papers in the map (data through 14 Oct 2026).
- Builds on 15 of its 21 references that are in the map.
- First posted on arXiv 7 Apr 2021.

## Builds on (most cited first, 15 of 15)

- [PyTorch: An Imperative Style, High-Performance Deep Learning Library](https://mlsanity.com/paper/1912.01703/) (2019)
- [Communication-Efficient Learning of Deep Networks from Decentralized Data](https://mlsanity.com/paper/1602.05629/) (2016)
- [Federated Optimization in Heterogeneous Networks](https://mlsanity.com/paper/1812.06127/) (2018)
- [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)
- [FedML: A Research Library and Benchmark for Federated Machine Learning](https://mlsanity.com/paper/2007.13518/) (2020)
- [TensorFlow Lite Micro: Embedded Machine Learning on TinyML Systems](https://mlsanity.com/paper/2010.08678/) (2020)
- [A generic framework for privacy preserving deep learning](https://mlsanity.com/paper/1811.04017/) (2018)
- [Highly Scalable Deep Learning Training System with Mixed-Precision: Training ImageNet in Four Minutes](https://mlsanity.com/paper/1807.11205/) (2018)
- [Visual Wake Words Dataset](https://mlsanity.com/paper/1906.05721/) (2019)
- [It's always personal: Using Early Exits for Efficient On-Device CNN Personalisation](https://mlsanity.com/paper/2102.01393/) (2021)
- [A Hitchhiker's Guide On Distributed Training of Deep Neural Networks](https://mlsanity.com/paper/1810.11787/) (2018)
- [Privacy-Preserving Bandits](https://mlsanity.com/paper/1909.04421/) (2019)
- [Heterogeneous Bitwidth Binarization in Convolutional Neural Networks](https://mlsanity.com/paper/1805.10368/) (2018)

## Built on by (most cited first, 13 of 13)

- [Machine Learning for Microcontroller-Class Hardware: A Review](https://mlsanity.com/paper/2205.14550/) (2022)
- [Evaluating Federated Learning for Intrusion Detection in Internet of Things: Review and Challenges](https://mlsanity.com/paper/2108.00974/) (2021)
- [p-Meta: Towards On-device Deep Model Adaptation](https://mlsanity.com/paper/2206.12705/) (2022)
- [FLSys: Toward an Open Ecosystem for Federated Learning Mobile Apps](https://mlsanity.com/paper/2111.09445/) (2021)
- [Breaking Physical and Linguistic Borders: Multilingual Federated Prompt Tuning for Low-Resource Languages](https://mlsanity.com/paper/2507.03003/) (2025)
- [Enhancing Efficiency in Multidevice Federated Learning through Data Selection](https://mlsanity.com/paper/2211.04175/) (2022)
- [Now It Sounds Like You: Learning Personalized Vocabulary On Device](https://mlsanity.com/paper/2305.03584/) (2023)
- [FedKit: Enabling Cross-Platform Federated Learning for Android and iOS](https://mlsanity.com/paper/2402.10464/) (2024)
- [On-device Federated Learning in Smartphones for Detecting Depression from Reddit Posts](https://mlsanity.com/paper/2410.13709/) (2024)
- [PeerFL: A Simulator for Peer-to-Peer Federated Learning at Scale](https://mlsanity.com/paper/2405.17839/) (2024)
- [RockNet: Distributed Learning on Ultra-Low-Power Devices](https://mlsanity.com/paper/2510.13320/) (2025)
- [Ed-Fed: A generic federated learning framework with resource-aware client selection for edge devices](https://mlsanity.com/paper/2307.07199/) (2023)
- [Unsupervised anomalies detection in IIoT edge devices networks using federated learning](https://mlsanity.com/paper/2308.12175/) (2023)

## Abstract (arXiv)

Federated Learning (FL) allows edge devices to collaboratively learn a shared prediction model while keeping their training data on the device, thereby decoupling the ability to do machine learning from the need to store data in the cloud. Despite the algorithmic advancements in FL, the support for on-device training of FL algorithms on edge devices remains poor. In this paper, we present an exploration of on-device FL on various smartphones and embedded devices using the Flower framework. We also evaluate the system costs of on-device FL and discuss how this quantification could be used to design more efficient FL algorithms.