mlsanityData to 7 Oct 2026

On-device Federated Learning with Flower

arXiv 2104.03042 ↗First posted 7 Apr 2021cs.LG cs.AI cs.DCcited by 13

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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.

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

Builds on

15 of 21 references.

References · 15

  1. 2019PyTorch: An Imperative Style, High-Performance Deep Learning Librarycited by 20,160
  2. 2016Communication-Efficient Learning of Deep Networks from Decentralized Datacited by 7,592
  3. 2018Federated Optimization in Heterogeneous Networkscited by 2,971
  4. 2019Towards Federated Learning at Scale: System Designcited by 826
  5. 2018LEAF: A Benchmark for Federated Settingscited by 780
  6. 2020Flower: A Friendly Federated Learning Research Frameworkcited by 449
  7. 2020FedML: A Research Library and Benchmark for Federated Machine Learningcited by 298
  8. 2020TensorFlow Lite Micro: Embedded Machine Learning on TinyML Systemscited by 161
  9. 2018A generic framework for privacy preserving deep learningcited by 103
  10. 2018Highly Scalable Deep Learning Training System with Mixed-Precision: Training ImageNet in Four Minutescited by 63
  11. 2019Visual Wake Words Datasetcited by 46
  12. 2021It's always personal: Using Early Exits for Efficient On-Device CNN Personalisationcited by 18
3 more
  1. 2018A Hitchhiker's Guide On Distributed Training of Deep Neural Networkscited by 7
  2. 2019Privacy-Preserving Banditscited by 6
  3. 2018Heterogeneous Bitwidth Binarization in Convolutional Neural Networkscited by 3

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Cited by year

Citing papers per year: 2021: 2; 2022: 3; 2023: 3; 2024: 3; 2025: 2.

13 papers.

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  1. 2022Machine Learning for Microcontroller-Class Hardware: A Reviewcited by 28
  2. 2021Evaluating Federated Learning for Intrusion Detection in Internet of Things: Review and Challengescited by 16
  3. 2022p-Meta: Towards On-device Deep Model Adaptationcited by 8
  4. 2021FLSys: Toward an Open Ecosystem for Federated Learning Mobile Appscited by 6
  5. 2025Breaking Physical and Linguistic Borders: Multilingual Federated Prompt Tuning for Low-Resource Languagescited by 5
  6. 2022Enhancing Efficiency in Multidevice Federated Learning through Data Selectioncited by 2
  7. 2023Now It Sounds Like You: Learning Personalized Vocabulary On Devicecited by 2
  8. 2024FedKit: Enabling Cross-Platform Federated Learning for Android and iOScited by 2
  9. 2024On-device Federated Learning in Smartphones for Detecting Depression from Reddit Postscited by 2
  10. 2024PeerFL: A Simulator for Peer-to-Peer Federated Learning at Scalecited by 1
  11. 2025RockNet: Distributed Learning on Ultra-Low-Power Devicescited by 1
  12. 2023Ed-Fed: A generic federated learning framework with resource-aware client selection for edge devicescited by 0
1 more
  1. 2023Unsupervised anomalies detection in IIoT edge devices networks using federated learningcited by 0