mlsanityData to 7 Oct 2026

Federated Semi-Supervised Learning with Annotation Heterogeneity

arXiv 2303.02445 ↗First posted 4 Mar 2023cs.LGcited by 0

Not on the map. Why?

Abstract (arXiv)

Federated Semi-Supervised Learning (FSSL) aims to learn a global model from different clients in an environment with both labeled and unlabeled data. Most of the existing FSSL work generally assumes that both types of data are available on each client. In this paper, we study a more general problem setup of FSSL with annotation heterogeneity, where each client can hold an arbitrary percentage (0%-100%) of labeled data. To this end, we propose a novel FSSL framework called Heterogeneously Annotated Semi-Supervised LEarning (HASSLE). Specifically, it is a dual-model framework with two models trained separately on labeled and unlabeled data such that it can be simply applied to a client with an arbitrary labeling percentage. Furthermore, a mutual learning strategy called Supervised-Unsupervised Mutual Alignment (SUMA) is proposed for the dual models within HASSLE with global residual alignment and model proximity alignment. Subsequently, the dual models can implicitly learn from both types of data across different clients, although each dual model is only trained locally on a single type of data. Experiments verify that the dual models in HASSLE learned by SUMA can mutually learn from each other, thereby effectively utilizing the information of both types of data across different clients.

Key facts. No citing papers in the map yet (data through 7 Oct 2026). Builds on 44 of its 67 references that are in the map. First posted on arXiv 4 Mar 2023.

Builds on

44 of 67 references.

References · 44

  1. 2015Deep Residual Learning for Image Recognitioncited by 51,809
  2. 2016Communication-Efficient Learning of Deep Networks from Decentralized Datacited by 7,592
  3. 2017Fashion-MNIST: a Novel Image Dataset for Benchmarking Machine Learning Algorithmscited by 4,283
  4. 2019Advances and Open Problems in Federated Learningcited by 3,021
  5. 2018Federated Optimization in Heterogeneous Networkscited by 2,971
  6. 2020FixMatch: Simplifying Semi-Supervised Learning with Consistency and Confidencecited by 2,080
  7. 2019MixMatch: A Holistic Approach to Semi-Supervised Learningcited by 1,390
  8. 2019Self-training with Noisy Student improves ImageNet classificationcited by 1,304
  9. 2019On the Convergence of FedAvg on Non-IID Datacited by 1,220
  10. 2018Federated Learning with Non-IID Datacited by 1,099
  11. 2019Unsupervised Data Augmentation for Consistency Trainingcited by 1,057
  12. 2016Temporal Ensembling for Semi-Supervised Learningcited by 1,054
32 more
  1. 2017Virtual Adversarial Training: A Regularization Method for Supervised and Semi-Supervised Learningcited by 918
  2. 2020Tackling the Objective Inconsistency Problem in Heterogeneous Federated Optimizationcited by 897
  3. 2021Model-Contrastive Federated Learningcited by 762
  4. 2020Ensemble Distillation for Robust Model Fusion in Federated Learningcited by 678
  5. 2020Ditto: Fair and Robust Federated Learning Through Personalizationcited by 665
  6. 2021Federated Learning on Non-IID Data Silos: An Experimental Studycited by 566
  7. 2018Federated Learning for Mobile Keyboard Predictioncited by 565
  8. 2021FedBN: Federated Learning on Non-IID Features via Local Batch Normalizationcited by 565
  9. 2019Pseudo-Labeling and Confirmation Bias in Deep Semi-Supervised Learningcited by 507
  10. 2019Clustered Federated Learning: Model-Agnostic Distributed Multi-Task Optimization under Privacy Constraintscited by 499
  11. 2021Towards Personalized Federated Learningcited by 462
  12. 2021FedProto: Federated Prototype Learning across Heterogeneous Clientscited by 349
  13. 2020SplitFed: When Federated Learning Meets Split Learningcited by 308
  14. 2020Personalized Cross-Silo Federated Learning on Non-IID Datacited by 282
  15. 2015Semi-Supervised Learning with Ladder Networkscited by 246
  16. 2018Communication-Efficient On-Device Machine Learning: Federated Distillation and Augmentation under Non-IID Private Datacited by 242
  17. 2019Model Pruning Enables Efficient Federated Learning on Edge Devicescited by 219
  18. 2019Model Fusion via Optimal Transportcited by 217
  19. 2020Distillation-Based Semi-Supervised Federated Learning for Communication-Efficient Collaborative Training with Non-IID Private Datacited by 125
  20. 2020Federated Semi-Supervised Learning with Inter-Client Consistency & Disjoint Learningcited by 112
  21. 2021Parameterized Knowledge Transfer for Personalized Federated Learningcited by 108
  22. 2021SimPLE: Similar Pseudo Label Exploitation for Semi-Supervised Classificationcited by 68
  23. 2020XOR Mixup: Privacy-Preserving Data Augmentation for One-Shot Federated Learningcited by 58
  24. 2021Federated Semi-supervised Medical Image Classification via Inter-client Relation Matchingcited by 42
  25. 2022FedX: Unsupervised Federated Learning with Cross Knowledge Distillationcited by 40
  26. 2022RSCFed: Random Sampling Consensus Federated Semi-supervised Learningcited by 32
  27. 2021SemiFed: Semi-supervised Federated Learning with Consistency and Pseudo-Labelingcited by 29
  28. 2020ResKD: Residual-Guided Knowledge Distillationcited by 25
  29. 2020Federated Semi-Supervised Learning for COVID Region Segmentation in Chest CT using Multi-National Data from China, Italy, Japancited by 23
  30. 2021Federated Learning with Taskonomy for Non-IID Datacited by 21
  31. 2021FedTriNet: A Pseudo Labeling Method with Three Players for Federated Semi-supervised Learningcited by 7
  32. 2021Federated Semi-Supervised Learning with Class Distribution Mismatchcited by 6

Built on by

Not cited yet.