mlsanityData to 14 Oct 2026

FederatedScope: A Flexible Federated Learning Platform for Heterogeneity

arXiv 2204.05011 ↗First posted 11 Apr 2022cs.LGcited by 53

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Abstract (arXiv)

Although remarkable progress has been made by existing federated learning (FL) platforms to provide infrastructures for development, these platforms may not well tackle the challenges brought by various types of heterogeneity, including the heterogeneity in participants' local data, resources, behaviors and learning goals. To fill this gap, in this paper, we propose a novel FL platform, named FederatedScope, which employs an event-driven architecture to provide users with great flexibility to independently describe the behaviors of different participants. Such a design makes it easy for users to describe participants with various local training processes, learning goals and backends, and coordinate them into an FL course with synchronous or asynchronous training strategies. Towards an easy-to-use and flexible platform, FederatedScope enables rich types of plug-in operations and components for efficient further development, and we have implemented several important components to better help users with privacy protection, attack simulation and auto-tuning. We have released FederatedScope at https://github.com/alibaba/FederatedScope to promote academic research and industrial deployment of federated learning in a wide range of scenarios.

Key facts. Cited by 53 papers in the map (data through 14 Oct 2026). Builds on 64 of its 114 references that are in the map. First posted on arXiv 11 Apr 2022.

Builds on

64 of 114 references.

References · 64

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38 more
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50 most cited of 64 All 64 (JSON)

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2022: 8; 2023: 16; 2024: 19; 2025: 7; 2026: 3.

53 papers.

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  1. 2023FederatedScope-LLM: A Comprehensive Package for Fine-tuning Large Language Models in Federated Learningcited by 149
  2. 2022Decentralized Federated Learning: Fundamentals, State of the Art, Frameworks, Trends, and Challengescited by 138
  3. 2023When Foundation Model Meets Federated Learning: Motivations, Challenges, and Future Directionscited by 84
  4. 2024FedBiOT: LLM Local Fine-tuning in Federated Learning without Full Modelcited by 74
  5. 2022pFL-Bench: A Comprehensive Benchmark for Personalized Federated Learningcited by 44
  6. 2022FederatedScope-GNN: Towards a Unified, Comprehensive and Efficient Package for Federated Graph Learningcited by 43
  7. 2023Efficient Personalized Federated Learning via Sparse Model-Adaptationcited by 38
  8. 2024A Survey on Efficient Federated Learning Methods for Foundation Model Trainingcited by 29
  9. 2023FS-Real: Towards Real-World Cross-Device Federated Learningcited by 22
  10. 2024On the Convergence of Zeroth-Order Federated Tuning for Large Language Modelscited by 19
  11. 2024Advances in APPFL: A Comprehensive and Extensible Federated Learning Frameworkcited by 19
  12. 2023PFLlib: A Beginner-Friendly and Comprehensive Personalized Federated Learning Library and Benchmarkcited by 17
38 more
  1. 2023Revisiting Personalized Federated Learning: Robustness Against Backdoor Attackscited by 16
  2. 2024From Challenges and Pitfalls to Recommendations and Opportunities: Implementing Federated Learning in Healthcarecited by 16
  3. 2024Synergizing Foundation Models and Federated Learning: A Surveycited by 14
  4. 2023MergeSFL: Split Federated Learning with Feature Merging and Batch Size Regulationcited by 13
  5. 2024Towards Federated RLHF with Aggregated Client Preference for LLMscited by 13
  6. 2023FLGo: A Fully Customizable Federated Learning Platformcited by 12
  7. 2024Open Challenges and Opportunities in Federated Foundation Models Towards Biomedical Healthcarecited by 12
  8. 2024Personalized Federated Fine-Tuning for LLMs via Data-Driven Heterogeneous Model Architecturescited by 12
  9. 2023Tunable Soft Prompts are Messengers in Federated Learningcited by 10
  10. 2022Auxo: Efficient Federated Learning via Scalable Client Clusteringcited by 8
  11. 2024pfl-research: simulation framework for accelerating research in Private Federated Learningcited by 8
  12. 2024COALA: A Practical and Vision-Centric Federated Learning Platformcited by 7
  13. 2024PII-Scope: A Comprehensive Study on Training Data PII Extraction Attacks in LLMscited by 7
  14. 2025HtFLlib: A Comprehensive Heterogeneous Federated Learning Library and Benchmarkcited by 7
  15. 2022FedHPO-B: A Benchmark Suite for Federated Hyperparameter Optimizationcited by 6
  16. 2023FederatedTrust: A Solution for Trustworthy Federated Learningcited by 6
  17. 2023Secure and Verifiable Data Collaboration with Low-Cost Zero-Knowledge Proofscited by 5
  18. 2024Personalized Federated Learning of Probabilistic Models: A PAC-Bayesian Approachcited by 5
  19. 2022Collaborating Heterogeneous Natural Language Processing Tasks via Federated Learningcited by 4
  20. 2023FedML Parrot: A Scalable Federated Learning System via Heterogeneity-aware Scheduling on Sequential and Hierarchical Trainingcited by 4
  21. 2024Exploring the Practicality of Federated Learning: A Survey Towards the Communication Perspectivecited by 4
  22. 2024HiFGL: A Hierarchical Framework for Cross-silo Cross-device Federated Graph Learningcited by 4
  23. 2022UniFed: All-In-One Federated Learning Platform to Unify Open-Source Frameworkscited by 3
  24. 2025Tree-based Models for Vertical Federated Learning: A Surveycited by 3
  25. 2023Survey of Federated Learning Models for Spatial-Temporal Mobility Applicationscited by 2
  26. 2022WrapperFL: A Model Agnostic Plug-in for Industrial Federated Learningcited by 1
  27. 2023HPN: Personalized Federated Hyperparameter Optimizationcited by 1
  28. 2025Integrating Identity-Based Identification against Adaptive Adversaries in Federated Learningcited by 1
  29. 2025FedMeNF: Privacy-Preserving Federated Meta-Learning for Neural Fieldscited by 1
  30. 2025Communication-Efficient and Accurate Approach for Aggregation in Federated Low-Rank Adaptationcited by 1
  31. 2026Scalable Cross-Facility Federated Learning for Scientific Foundation Models on Multiple Supercomputerscited by 1
  32. 2023A Comprehensive Empirical Study of Bugs in Open-Source Federated Learning Frameworkscited by 0
  33. 2023Personalized Federated Learning via ADMM with Moreau Envelopecited by 0
  34. 2024A Bargaining-based Approach for Feature Trading in Vertical Federated Learningcited by 0
  35. 2024TablePuppet: A Generic Framework for Relational Federated Learningcited by 0
  36. 2024Federated Computing -- Survey on Building Blocks, Extensions and Systemscited by 0
  37. 2024Where is the Testbed for my Federated Learning Research?cited by 0
  38. 2025MedSegNet10: A Publicly Accessible Network Repository for Split Federated Medical Image Segmentationcited by 0

50 most cited of 53 All 53 (JSON)