mlsanityData to 7 Oct 2026

FLGo: A Fully Customizable Federated Learning Platform

arXiv 2306.12079 ↗First posted 21 Jun 2023cs.LG cs.DCcited by 12

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Abstract (arXiv)

Federated learning (FL) has found numerous applications in healthcare, finance, and IoT scenarios. Many existing FL frameworks offer a range of benchmarks to evaluate the performance of FL under realistic conditions. However, the process of customizing simulations to accommodate application-specific settings, data heterogeneity, and system heterogeneity typically remains unnecessarily complicated. This creates significant hurdles for traditional ML researchers in exploring the usage of FL, while also compromising the shareability of codes across FL frameworks. To address this issue, we propose a novel lightweight FL platform called FLGo, to facilitate cross-application FL studies with a high degree of shareability. Our platform offers 40+ benchmarks, 20+ algorithms, and 2 system simulators as out-of-the-box plugins. We also provide user-friendly APIs for quickly customizing new plugins that can be readily shared and reused for improved reproducibility. Finally, we develop a range of experimental tools, including parallel acceleration, experiment tracker and analyzer, and parameters auto-tuning. FLGo is maintained at urlflgo-xmu.github.io.

Key facts. Cited by 12 papers in the map (data through 7 Oct 2026). Builds on 24 of its 37 references that are in the map. First posted on arXiv 21 Jun 2023.

Builds on

24 of 37 references.

References · 24

  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. 2020Tackling the Objective Inconsistency Problem in Heterogeneous Federated Optimizationcited by 897
  5. 2019Measuring the Effects of Non-Identical Data Distribution for Federated Visual Classificationcited by 836
  6. 2019Towards Federated Learning at Scale: System Designcited by 826
  7. 2018LEAF: A Benchmark for Federated Settingscited by 780
  8. 2021Federated Learning on Non-IID Data Silos: An Experimental Studycited by 566
  9. 2020Flower: A Friendly Federated Learning Research Frameworkcited by 449
  10. 2020FedML: A Research Library and Benchmark for Federated Machine Learningcited by 298
  11. 2021FedDG: Federated Domain Generalization on Medical Image Segmentation via Episodic Learning in Continuous Frequency Spacecited by 284
  12. 2019Asynchronous Federated Optimizationcited by 264
12 more
  1. 2021Federated Learning for Internet of Things: A Comprehensive Surveycited by 247
  2. 2019Client-Edge-Cloud Hierarchical Federated Learningcited by 202
  3. 2021Federated Learning with Buffered Asynchronous Aggregationcited by 188
  4. 2021Federated Learning for Open Bankingcited by 138
  5. 2022Decentralized Federated Learning: Fundamentals, State of the Art, Frameworks, Trends, and Challengescited by 137
  6. 2021FedScale: Benchmarking Model and System Performance of Federated Learning at Scalecited by 135
  7. 2021Subgraph Federated Learning with Missing Neighbor Generationcited by 132
  8. 2018A generic framework for privacy preserving deep learningcited by 103
  9. 2020TiFL: A Tier-based Federated Learning Systemcited by 95
  10. 2021Federated Learning with Fair Averagingcited by 59
  11. 2022FederatedScope: A Flexible Federated Learning Platform for Heterogeneitycited by 53
  12. 2022FedGS: Federated Graph-based Sampling with Arbitrary Client Availabilitycited by 10

Built on by

Cited by year (2026 to 7 Oct)

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

12 papers.

  1. 2024Advances in APPFL: A Comprehensive and Extensible Federated Learning Frameworkcited by 19
  2. 2024FedPFT: Federated Proxy Fine-Tuning of Foundation Modelscited by 16
  3. 2024COALA: A Practical and Vision-Centric Federated Learning Platformcited by 7
  4. 2025Federated Learning with Domain Shift Erasercited by 7
  5. 2025Privacy-Preserving Federated Embedding Learning for Localized Retrieval-Augmented Generationcited by 7
  6. 2025Corrected with the Latest Version: Make Robust Asynchronous Federated Learning Possiblecited by 2
  7. 2025AFBS:Buffer Gradient Selection in Semi-asynchronous Federated Learningcited by 2
  8. 2024Selective Knowledge Sharing for Personalized Federated Learning Under Capacity Heterogeneitycited by 0
  9. 2024FedModule: A Modular Federated Learning Frameworkcited by 0
  10. 2025FedSM: Robust Semantics-Guided Feature Mixup for Bias Reduction in Federated Learning with Long-Tail Datacited by 0
  11. 2026FedPSA: Modeling Behavioral Staleness in Asynchronous Federated Learningcited by 0
  12. 2026Decoupled Visual Processing: Efficient Multimodal Adaptation via Modality-Specific Transformer Substitutioncited by 0