mlsanityData to 14 Oct 2026

FedAIoT: A Federated Learning Benchmark for Artificial Intelligence of Things

arXiv 2310.00109 ↗First posted 29 Sep 2023cs.LG cs.DC cs.DLcited by 8

Not on the map. Why?

Abstract (arXiv)

There is a significant relevance of federated learning (FL) in the realm of Artificial Intelligence of Things (AIoT). However, most existing FL works do not use datasets collected from authentic IoT devices and thus do not capture unique modalities and inherent challenges of IoT data. To fill this critical gap, in this work, we introduce FedAIoT, an FL benchmark for AIoT. FedAIoT includes eight datasets collected from a wide range of IoT devices. These datasets cover unique IoT modalities and target representative applications of AIoT. FedAIoT also includes a unified end-to-end FL framework for AIoT that simplifies benchmarking the performance of the datasets. Our benchmark results shed light on the opportunities and challenges of FL for AIoT. We hope FedAIoT could serve as an invaluable resource to foster advancements in the important field of FL for AIoT. The repository of FedAIoT is maintained at https://github.com/AIoT-MLSys-Lab/FedAIoT.

Key facts. Cited by 8 papers in the map (data through 14 Oct 2026). Builds on 52 of its 89 references that are in the map. First posted on arXiv 29 Sep 2023.

Builds on

52 of 89 references.

References · 52

  1. 2019PyTorch: An Imperative Style, High-Performance Deep Learning Librarycited by 20,305
  2. 2015Distilling the Knowledge in a Neural Networkcited by 10,324
  3. 2014ImageNet Large Scale Visual Recognition Challengecited by 8,498
  4. 2016Communication-Efficient Learning of Deep Networks from Decentralized Datacited by 7,669
  5. 2017mixup: Beyond Empirical Risk Minimizationcited by 5,183
  6. 2017Fashion-MNIST: a Novel Image Dataset for Benchmarking Machine Learning Algorithmscited by 4,349
  7. 2016Pointer Sentinel Mixture Modelscited by 3,096
  8. 2019Advances and Open Problems in Federated Learningcited by 3,046
  9. 2018Federated Optimization in Heterogeneous Networkscited by 2,987
  10. 2015Character-level Convolutional Networks for Text Classificationcited by 2,330
  11. 2017Quantization and Training of Neural Networks for Efficient Integer-Arithmetic-Only Inferencecited by 1,593
  12. 2019Federated Learning: Challenges, Methods, and Future Directionscited by 1,538
38 more
  1. 2017Mixed Precision Trainingcited by 1,156
  2. 2018Federated Learning with Non-IID Datacited by 1,108
  3. 2020Adaptive Federated Optimizationcited by 1,046
  4. 2019Measuring the Effects of Non-Identical Data Distribution for Federated Visual Classificationcited by 851
  5. 2018Speech Commands: A Dataset for Limited-Vocabulary Speech Recognitioncited by 752
  6. 2020Ensemble Distillation for Robust Model Fusion in Federated Learningcited by 682
  7. 2016Making Deep Neural Networks Robust to Label Noise: a Loss Correction Approachcited by 665
  8. 2020Learning from Noisy Labels with Deep Neural Networks: A Surveycited by 582
  9. 2019Federated Learning with Personalization Layerscited by 541
  10. 2017Ray: A Distributed Framework for Emerging AI Applicationscited by 527
  11. 2019Robust and Communication-Efficient Federated Learning from Non-IID Datacited by 419
  12. 2020HeteroFL: Computation and Communication Efficient Federated Learning for Heterogeneous Clientscited by 337
  13. 2014Training Deep Neural Networks on Noisy Labels with Bootstrappingcited by 322
  14. 2021A Field Guide to Federated Optimizationcited by 315
  15. 2018Joint Optimization Framework for Learning with Noisy Labelscited by 293
  16. 2020Detection and Tracking Meet Drones Challengecited by 263
  17. 2017Incremental Network Quantization: Towards Lossless CNNs with Low-Precision Weightscited by 217
  18. 2016Ternary Weight Networkscited by 204
  19. 2023A Comprehensive Review of YOLO Architectures in Computer Vision: From YOLOv1 to YOLOv8 and YOLO-NAScited by 151
  20. 2020Distillation-Based Semi-Supervised Federated Learning for Communication-Efficient Collaborative Training with Non-IID Private Datacited by 125
  21. 2022FLamby: Datasets and Benchmarks for Cross-Silo Federated Learning in Realistic Healthcare Settingscited by 125
  22. 2022FedRolex: Model-Heterogeneous Federated Learning with Rolling Sub-Model Extractioncited by 102
  23. 2022DataPerf: Benchmarks for Data-Centric AI Developmentcited by 90
  24. 2021FedNLP: Benchmarking Federated Learning Methods for Natural Language Processing Taskscited by 85
  25. 2022Heterogeneous Ensemble Knowledge Transfer for Training Large Models in Federated Learningcited by 81
  26. 2022FedCorr: Multi-Stage Federated Learning for Label Noise Correctioncited by 61
  27. 2023FedMultimodal: A Benchmark For Multimodal Federated Learningcited by 51
  28. 2023Epic-Sounds: A Large-scale Dataset of Actions That Soundcited by 47
  29. 2022SenseFi: A Library and Benchmark on Deep-Learning-Empowered WiFi Human Sensingcited by 34
  30. 2023FedNoRo: Towards Noise-Robust Federated Learning by Addressing Class Imbalance and Label Noise Heterogeneitycited by 33
  31. 2021QuPeD: Quantized Personalization via Distillation with Applications to Federated Learningcited by 32
  32. 2021FedCV: A Federated Learning Framework for Diverse Computer Vision Taskscited by 32
  33. 2023Towards Personalized Federated Learning via Heterogeneous Model Reassemblycited by 31
  34. 2022FLUTE: A Scalable, Extensible Framework for High-Performance Federated Learning Simulationscited by 27
  35. 2023GPT-FL: Generative Pre-trained Model-Assisted Federated Learningcited by 24
  36. 2022DYNAFED: Tackling Client Data Heterogeneity with Global Dynamicscited by 22
  37. 2022FedAudio: A Federated Learning Benchmark for Audio Taskscited by 16
  38. 2022Labeling Chaos to Learning Harmony: Federated Learning with Noisy Labelscited by 13

50 most cited of 52 All 52 (JSON)

Built on by

Cited by year (2026 to 14 Oct)

Citing papers per year: 2023: 1; 2024: 3; 2025: 2; 2026: 2.

8 papers.

  1. 2023AdaptiveFL: Adaptive Heterogeneous Federated Learning for Resource-Constrained AIoT Systemscited by 9
  2. 2024The Internet of Things in the Era of Generative AI: Vision and Challengescited by 8
  3. 2024COALA: A Practical and Vision-Centric Federated Learning Platformcited by 7
  4. 2024Tazza: Shuffling Neural Network Parameters for Secure and Private Federated Learningcited by 2
  5. 2025FedGrAINS: Personalized SubGraph Federated Learning with Adaptive Neighbor Samplingcited by 1
  6. 2025FedPaI: Achieving Extreme Sparsity in Federated Learning via Pruning at Initializationcited by 1
  7. 2026Flower Hub: A Reproducible Benchmarking Platform for Federated Learning in Simulation and Deploymentcited by 0
  8. 2026PrivateHub: Contrastive Diffusion Model for Private Sensor-Intensive Environment Data Generationcited by 0