mlsanityData to 14 Oct 2026

Robust Semi-supervised Federated Learning for Images Automatic Recognition in Internet of Drones

arXiv 2201.01230 ↗First posted 3 Jan 2022cs.LG cs.AI cs.CVcited by 7

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Abstract (arXiv)

Air access networks have been recognized as a significant driver of various Internet of Things (IoT) services and applications. In particular, the aerial computing network infrastructure centered on the Internet of Drones has set off a new revolution in automatic image recognition. This emerging technology relies on sharing ground truth labeled data between Unmanned Aerial Vehicle (UAV) swarms to train a high-quality automatic image recognition model. However, such an approach will bring data privacy and data availability challenges. To address these issues, we first present a Semi-supervised Federated Learning (SSFL) framework for privacy-preserving UAV image recognition. Specifically, we propose model parameters mixing strategy to improve the naive combination of FL and semi-supervised learning methods under two realistic scenarios (labels-at-client and labels-at-server), which is referred to as Federated Mixing (FedMix). Furthermore, there are significant differences in the number, features, and distribution of local data collected by UAVs using different camera modules in different environments, i.e., statistical heterogeneity. To alleviate the statistical heterogeneity problem, we propose an aggregation rule based on the frequency of the client's participation in training, namely the FedFreq aggregation rule, which can adjust the weight of the corresponding local model according to its frequency. Numerical results demonstrate that the performance of our proposed method is significantly better than those of the current baseline and is robust to different non-IID levels of client data.

Key facts. Cited by 7 papers in the map (data through 14 Oct 2026). Builds on 28 of its 50 references that are in the map. First posted on arXiv 3 Jan 2022.

Builds on

28 of 50 references.

References · 28

  1. 2016Communication-Efficient Learning of Deep Networks from Decentralized Datacited by 7,669
  2. 2018Federated Optimization in Heterogeneous Networkscited by 2,987
  3. 2019On the Convergence of FedAvg on Non-IID Datacited by 1,229
  4. 2018Federated Learning with Non-IID Datacited by 1,108
  5. 2019Unsupervised Data Augmentation for Consistency Trainingcited by 1,060
  6. 2016Temporal Ensembling for Semi-Supervised Learningcited by 1,057
  7. 2020Tackling the Objective Inconsistency Problem in Heterogeneous Federated Optimizationcited by 906
  8. 2019Measuring the Effects of Non-Identical Data Distribution for Federated Visual Classificationcited by 851
  9. 2018Overcoming catastrophic forgetting with hard attention to the taskcited by 681
  10. 2021FedBN: Federated Learning on Non-IID Features via Local Batch Normalizationcited by 571
  11. 2021Federated Learning on Non-IID Data Silos: An Experimental Studycited by 568
  12. 2014Semi-Supervised Learning with Deep Generative Modelscited by 496
16 more
  1. 2016Regularization With Stochastic Transformations and Perturbations for Deep Semi-Supervised Learningcited by 441
  2. 2019Robust and Communication-Efficient Federated Learning from Non-IID Datacited by 419
  3. 2019S4L: Self-Supervised Semi-Supervised Learningcited by 335
  4. 2021A Survey on Deep Semi-supervised Learningcited by 251
  5. 2020Federated learning with hierarchical clustering of local updates to improve training on non-IID datacited by 223
  6. 2020Distillation-Based Semi-Supervised Federated Learning for Communication-Efficient Collaborative Training with Non-IID Private Datacited by 125
  7. 2020Federated Semi-Supervised Learning with Inter-Client Consistency & Disjoint Learningcited by 112
  8. 2020Privacy-preserving Traffic Flow Prediction: A Federated Learning Approachcited by 83
  9. 2017Adversarial Dropout for Supervised and Semi-supervised Learningcited by 39
  10. 2020Federated Learning for 6G Communications: Challenges, Methods, and Future Directionscited by 33
  11. 2020A Secure Federated Learning Framework for 5G Networkscited by 32
  12. 2020Towards Federated Learning in UAV-Enabled Internet of Vehicles: A Multi-Dimensional Contract-Matching Approachcited by 29
  13. 2020Towards Utilizing Unlabeled Data in Federated Learning: A Survey and Prospectivecited by 27
  14. 2020Federated Learning in the Sky: Aerial-Ground Air Quality Sensing Framework with UAV Swarmscited by 26
  15. 2021Resource-constrained Federated Edge Learning with Heterogeneous Data: Formulation and Analysiscited by 10
  16. 2020Towards Communication-efficient and Attack-Resistant Federated Edge Learning for Industrial Internet of Thingscited by 9

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Cited by year

Citing papers per year: 2023: 6; 2024: 1.

7 papers.

  1. 2023Generalized Video Anomaly Event Detection: Systematic Taxonomy and Comparison of Deep Modelscited by 59
  2. 2023Federated Learning in Intelligent Transportation Systems: Recent Applications and Open Problemscited by 15
  3. 2024Mobility-Aware Federated Self-supervised Learning in Vehicular Networkcited by 9
  4. 2023Semi-Federated Learning for Collaborative Intelligence in Massive IoT Networkscited by 7
  5. 2023Federated Learning: A Cutting-Edge Survey of the Latest Advancements and Applicationscited by 3
  6. 2023Data-Efficient Energy-Aware Participant Selection for UAV-Enabled Federated Learningcited by 2
  7. 2023Federated Learning in UAV-Enhanced Networks: Joint Coverage and Convergence Time Optimizationcited by 1