mlsanityData to 7 Oct 2026

Towards Open Federated Learning Platforms: Survey and Vision from Technical and Legal Perspectives

arXiv 2307.02140 ↗First posted 5 Jul 2023cs.SE cs.AI cs.LGcited by 3

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Abstract (arXiv)

Traditional Federated Learning (FL) follows a server-dominated cooperation paradigm which narrows the application scenarios of FL and decreases the enthusiasm of data holders to participate. To fully unleash the potential of FL, we advocate rethinking the design of current FL frameworks and extending it to a more generalized concept: Open Federated Learning Platforms, positioned as a crowdsourcing collaborative machine learning infrastructure for all Internet users. We propose two reciprocal cooperation frameworks to achieve this: query-based FL and contract-based FL. In this survey, we conduct a comprehensive review of the feasibility of constructing open FL platforms from both technical and legal perspectives. We begin by reviewing the definition of FL and summarizing its inherent limitations, including server-client coupling, low model reusability, and non-public. In particular, we introduce a novel taxonomy to streamline the analysis of model license compatibility in FL studies that involve batch model reusing methods, including combination, amalgamation, distillation, and generation. This taxonomy provides a feasible solution for identifying the corresponding licenses clauses and facilitates the analysis of potential legal implications and restrictions when reusing models. Through this survey, we uncover the current dilemmas faced by FL and advocate for the development of sustainable open FL platforms. We aim to provide guidance for establishing such platforms in the future while identifying potential limitations that need to be addressed.

Key facts. Cited by 3 papers in the map (data through 7 Oct 2026). Builds on 175 of its 317 references that are in the map. First posted on arXiv 5 Jul 2023.

Builds on

175 of 317 references.

References · 175

  1. 2018BERT: Pre-training of Deep Bidirectional Transformers for Language Understandingcited by 40,204
  2. 2021High-Resolution Image Synthesis with Latent Diffusion Modelscited by 18,694
  3. 2022Training language models to follow instructions with human feedbackcited by 15,712
  4. 2014Very Deep Convolutional Networks for Large-Scale Image Recognitioncited by 15,267
  5. 2019RoBERTa: A Robustly Optimized BERT Pretraining Approachcited by 13,266
  6. 2015Distilling the Knowledge in a Neural Networkcited by 10,076
  7. 2016Communication-Efficient Learning of Deep Networks from Decentralized Datacited by 7,592
  8. 2022Generative Adversarial Networkscited by 5,603
  9. 2023Adding Conditional Control to Text-to-Image Diffusion Modelscited by 5,466
  10. 2017mixup: Beyond Empirical Risk Minimizationcited by 5,155
  11. 2016SQuAD: 100,000+ Questions for Machine Comprehension of Textcited by 4,115
  12. 2013Auto-Encoding Variational Bayescited by 3,980
38 more
  1. 2022OPT: Open Pre-trained Transformer Language Modelscited by 3,464
  2. 2019Advances and Open Problems in Federated Learningcited by 3,021
  3. 2018Federated Optimization in Heterogeneous Networkscited by 2,971
  4. 2015Fast R-CNNcited by 2,930
  5. 2022BLOOM: A 176B-Parameter Open-Access Multilingual Language Modelcited by 1,968
  6. 2014FitNets: Hints for Thin Deep Netscited by 1,619
  7. 2019Federated Learning: Challenges, Methods, and Future Directionscited by 1,531
  8. 2016Federated Learning: Strategies for Improving Communication Efficiencycited by 1,461
  9. 2019On the Convergence of FedAvg on Non-IID Datacited by 1,220
  10. 2021Unsupervised Dense Information Retrieval with Contrastive Learningcited by 1,203
  11. 2019Deep Leakage from Gradientscited by 1,004
  12. 2022CodeGen: An Open Large Language Model for Code with Multi-Turn Program Synthesiscited by 876
  13. 2019Towards Federated Learning at Scale: System Designcited by 826
  14. 2023StarCoder: may the source be with you!cited by 790
  15. 2018LEAF: A Benchmark for Federated Settingscited by 780
  16. 2021Model-Contrastive Federated Learningcited by 762
  17. 2018How To Backdoor Federated Learningcited by 745
  18. 2022Galactica: A Large Language Model for Sciencecited by 736
  19. 2020Ensemble Distillation for Robust Model Fusion in Federated Learningcited by 678
  20. 2020Ditto: Fair and Robust Federated Learning Through Personalizationcited by 665
  21. 2018Deep Visual Domain Adaptation: A Surveycited by 634
  22. 2020The Future of Digital Health with Federated Learningcited by 625
  23. 2021Federated Learning on Non-IID Data Silos: An Experimental Studycited by 566
  24. 2018Federated Learning for Mobile Keyboard Predictioncited by 565
  25. 2020Federated Learning with Matched Averagingcited by 564
  26. 2018Born Again Neural Networkscited by 539
  27. 2022Precise Zero-Shot Dense Retrieval without Relevance Labelscited by 508
  28. 2022LayoutLMv3: Pre-training for Document AI with Unified Text and Image Maskingcited by 481
  29. 2019Local Model Poisoning Attacks to Byzantine-Robust Federated Learningcited by 479
  30. 2021Towards Personalized Federated Learningcited by 462
  31. 2019FedMD: Heterogenous Federated Learning via Model Distillationcited by 454
  32. 2017Differentially Private Federated Learning: A Client Level Perspectivecited by 451
  33. 2020Flower: A Friendly Federated Learning Research Frameworkcited by 449
  34. 2017Efficient Processing of Deep Neural Networks: A Tutorial and Surveycited by 427
  35. 2019Robust and Communication-Efficient Federated Learning from Non-IID Datacited by 415
  36. 2019Federated Learning in Mobile Edge Networks: A Comprehensive Surveycited by 407
  37. 2018Split learning for health: Distributed deep learning without sharing raw patient datacited by 391
  38. 2021Pervasive Label Errors in Test Sets Destabilize Machine Learning Benchmarkscited by 388

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Citing papers per year: 2024: 2; 2025: 1.

3 papers.

  1. 2024One-Shot Sequential Federated Learning for Non-IID Data by Enhancing Local Model Diversitycited by 8
  2. 2024Data-Free Federated Class Incremental Learning with Diffusion-Based Generative Memorycited by 3
  3. 2025Mosaic: Data-Free Knowledge Distillation via Mixture-of-Experts for Heterogeneous Distributed Environmentscited by 2