mlsanityData to 7 Oct 2026

FLINT: A Platform for Federated Learning Integration

arXiv 2302.12862 ↗First posted 24 Feb 2023cs.LG cs.DCcited by 17

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Abstract (arXiv)

Cross-device federated learning (FL) has been well-studied from algorithmic, system scalability, and training speed perspectives. Nonetheless, moving from centralized training to cross-device FL for millions or billions of devices presents many risks, including performance loss, developer inertia, poor user experience, and unexpected application failures. In addition, the corresponding infrastructure, development costs, and return on investment are difficult to estimate. In this paper, we present a device-cloud collaborative FL platform that integrates with an existing machine learning platform, providing tools to measure real-world constraints, assess infrastructure capabilities, evaluate model training performance, and estimate system resource requirements to responsibly bring FL into production. We also present a decision workflow that leverages the FL-integrated platform to comprehensively evaluate the trade-offs of cross-device FL and share our empirical evaluations of business-critical machine learning applications that impact hundreds of millions of users.

Key facts. Cited by 17 papers in the map (data through 7 Oct 2026). Builds on 35 of its 48 references that are in the map. First posted on arXiv 24 Feb 2023.

Builds on

35 of 48 references.

References · 35

  1. 2016Communication-Efficient Learning of Deep Networks from Decentralized Datacited by 7,592
  2. 2019Advances and Open Problems in Federated Learningcited by 3,021
  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. 2020Fast is better than free: Revisiting adversarial trainingcited by 723
  7. 2021Federated Learning on Non-IID Data Silos: An Experimental Studycited by 566
  8. 2018Federated Learning for Mobile Keyboard Predictioncited by 565
  9. 2017Ray: A Distributed Framework for Emerging AI Applicationscited by 516
  10. 2020FedML: A Research Library and Benchmark for Federated Machine Learningcited by 298
  11. 2018Parallel Restarted SGD with Faster Convergence and Less Communication: Demystifying Why Model Averaging Works for Deep Learningcited by 277
  12. 2018Horovod: fast and easy distributed deep learning in TensorFlowcited by 275
23 more
  1. 2019Can You Really Backdoor Federated Learning?cited by 250
  2. 2021Sustainable AI: Environmental Implications, Challenges and Opportunitiescited by 242
  3. 2018Applied Federated Learning: Improving Google Keyboard Query Suggestionscited by 202
  4. 2021FjORD: Fair and Accurate Federated Learning under heterogeneous targets with Ordered Dropoutcited by 192
  5. 2021Federated Learning with Buffered Asynchronous Aggregationcited by 188
  6. 2021FedScale: Benchmarking Model and System Performance of Federated Learning at Scalecited by 135
  7. 2021Back to the Drawing Board: A Critical Evaluation of Poisoning Attacks on Production Federated Learningcited by 116
  8. 2019Federated Learning for Emoji Prediction in a Mobile Keyboardcited by 115
  9. 2021PPFL: Privacy-preserving Federated Learning with Trusted Execution Environmentscited by 93
  10. 2021Papaya: Practical, Private, and Scalable Federated Learningcited by 89
  11. 2021Federated Evaluation and Tuning for On-Device Personalization: System Design & Applicationscited by 84
  12. 2021On Large-Cohort Training for Federated Learningcited by 73
  13. 2021LightSecAgg: a Lightweight and Versatile Design for Secure Aggregation in Federated Learningcited by 63
  14. 2021TT-Rec: Tensor Train Compression for Deep Learning Recommendation Modelscited by 58
  15. 2019Federated Learning Of Out-Of-Vocabulary Wordscited by 51
  16. 2022FLUTE: A Scalable, Extensible Framework for High-Performance Federated Learning Simulationscited by 27
  17. 2019Federated Learning for Ranking Browser History Suggestionscited by 23
  18. 2022Walle: An End-to-End, General-Purpose, and Large-Scale Production System for Device-Cloud Collaborative Machine Learningcited by 23
  19. 2016KeystoneML: Optimizing Pipelines for Large-Scale Advanced Analyticscited by 13
  20. 2022Learning Compressed Embeddings for On-Device Inferencecited by 13
  21. 2022A Tale of Two Models: Constructing Evasive Attacks on Edge Modelscited by 6
  22. 2022On Noisy Evaluation in Federated Hyperparameter Tuningcited by 6
  23. 2021Deep Natural Language Processing for LinkedIn Searchcited by 3

Built on by

Cited by year (2026 to 7 Oct)

Citing papers per year: 2023: 5; 2024: 7; 2025: 3; 2026: 2.

17 papers.

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  1. 2024A Survey on Efficient Federated Learning Methods for Foundation Model Trainingcited by 29
  2. 2023Green Federated Learningcited by 8
  3. 2024COALA: A Practical and Vision-Centric Federated Learning Platformcited by 7
  4. 2023Monitoring and Adapting ML Models on Mobile Devicescited by 6
  5. 2023FLEdge: Benchmarking Federated Machine Learning Applications in Edge Computing Systemscited by 3
  6. 2023Towards Open Federated Learning Platforms: Survey and Vision from Technical and Legal Perspectivescited by 3
  7. 2024Momentum Approximation in Asynchronous Private Federated Learningcited by 3
  8. 2025Research in Collaborative Learning Does Not Serve Cross-Silo Federated Learning in Practicecited by 3
  9. 2023Venn: Resource Management for Collaborative Learning Jobscited by 1
  10. 2024FedTrans: Efficient Federated Learning via Multi-Model Transformationcited by 1
  11. 2024PAPAYA Federated Analytics Stack: Engineering Privacy, Scalability and Practicalitycited by 1
  12. 2024PerOS: Personalized Self-Adapting Operating Systems in the Cloudcited by 0
5 more
  1. 2024Photon: Federated LLM Pre-Trainingcited by 0
  2. 2025Leveraging Label Potential for Enhanced Multimodal Emotion Recognitioncited by 0
  3. 2025EZYer: A simulacrum of high school with generative agentcited by 0
  4. 2026Robust Synchronisation for Federated Learning in The Face of Correlated Device Failurecited by 0
  5. 2026Robust Federated Learning Under Real-World Client Churncited by 0