Abstract
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Wenyi Zhang, Xiangjie Kong, Junyi Xie, Xu Guo, Guojiang Shen
Abstract
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Active learning based federated learning for waste and natural disaster image classification
10.1109/access.2020.3038676 · 2020
Federated active learning (f-AL): An efficient annotation strategy for federated learning
10.1109/access.2024.3376746 · 2024
Video summarization using deep neural networks: A survey
10.1109/jproc.2021.3117472 · 2021
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Combating data imbalances in federated semi-supervised learning with dual regulators
2024
Adaptive test-time personalization for federated learning
2023
Federated continual learning for task-incremental and class-incremental problems: A survey
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openalex
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FedTriNet: A pseudo labeling method with three players for federated semi-supervised learning
2021
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2024
Think twice before selection: Federated evidential active learning for medical image analysis with domain shifts
2024
A simple framework for contrastive learning of visual representations
2020
Exploring simple siamese representation learning
2021
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Towards federated large language models: Motivations, methods, and future directions
10.1109/comst.2024.3503680 · 2025
Local or global: Selective knowledge assimilation for federated learning with limited labels
2023
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10.1007/978-94-6265-282-8 · 2019
Scalable watermarking for identifying large language model outputs
10.1038/s41586-024-08025-4 · 2024
SemiFL: Semi-supervised federated learning for unlabeled clients with alternate training
2022
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Robust federated learning with noisy and heterogeneous clients
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Federated learning for mobility applications
10.1145/3637868 · 2024
Training deep neural-networks using a noise adaptation layer
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AutoDO: Robust autoaugment for biased data with label noise via scalable probabilistic implicit differentiation
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Cooperative pseudo labeling for unsupervised federated classification
2025
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Unleashing the prospective of blockchain-federated learning fusion for iot security: A comprehensive review
10.1016/j.cosrev.2024.100685 · 2024
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Federated semi-supervised calibrated efficient fine-tuning of foundation models for medical image classification
2025
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10.1007/s13042-024-02119-1 · 2024
FedFixer: Mitigating heterogeneous label noise in federated learning
2024
A comprehensive survey on communication-efficient federated learning in mobile edge environments
10.1109/comst.2025.3535957 · 2025
A survey of blockchain and artificial intelligence for 6g wireless communications
10.1109/comst.2023.3315374 · doi-reference
Privacy-enhanced prototype-based federated cross-modal hashing for cross-modal retrieval
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Semi-supervised federated learning for travel mode identification from GPS trajectories
10.1109/tits.2021.3092015 · doi-reference
Multimodal sentiment analysis with unimodal label generation and modality decomposition
10.1016/j.inffus.2024.102787 · doi-reference
MedFedProto: A semi-supervised classification framework for medical images based on federated prototypical learning
10.1016/j.eswa.2025.130332 · doi-reference
A game-theoretic federated learning framework for data quality improvement
10.1109/tkde.2022.3230959 · doi-reference
Federated unsupervised representation learning
10.1631/fitee.2200268 · doi-reference
Prototype similarity distillation for communication-efficient federated unsupervised representation learning
10.1109/tkde.2024.3386712 · doi-reference
Heterogeneous federated learning: State-of-the-art and research challenges
10.1145/3625558 · doi-reference
Cross-modal federated human activity recognition
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A survey on deep semi-supervised learning
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Robust federated learning with noisy labels
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Client selection for federated learning with label noise
10.1109/tvt.2021.3131852 · doi-reference
Federated semi-supervised learning for COVID region segmentation in chest CT using multi-national data from china, italy, japan
10.1016/j.media.2021.101992 · doi-reference
Enhancing federated learning with server-side unlabeled data by adaptive client and data selection
10.1109/tmc.2023.3265010 · doi-reference
Overcoming noisy labels and non-IID data in edge federated learning
10.1109/tmc.2024.3398801 · doi-reference
A survey of label-efficient deep learning for 3d point clouds
10.1109/tpami.2024.3416302 · doi-reference
Balanced federated semisupervised learning with fairness-aware pseudo-labeling
10.1109/tnnls.2022.3233093 · doi-reference
Federated semi-supervised learning with tolerant guidance and powerful classifier in edge scenarios
10.1016/j.ins.2024.120201 · doi-reference
Unsupervised federated optimization at the edge: D2d-enabled learning without labels
10.1109/tccn.2024.3392792 · doi-reference
Leveraging unlabeled data in federated learning: A review
10.1109/access.2025.3623444 · doi-reference
A systematic literature review of robust federated learning: Issues, solutions, and future research directions
10.1145/3727643 · doi-reference
A survey on label-efficient deep image segmentation: Bridging the gap between weak supervision and dense prediction
10.1109/tpami.2023.3246102 · doi-reference
Industrial defect detection for data privacy and domain differences: Based on federated learning and unsupervised domain adaptation
10.1016/j.eswa.2026.131265 · doi-reference
Advances and open challenges in federated foundation models
10.1109/comst.2025.3552524 · doi-reference
Semi-supervised and personalized federated activity recognition based on active learning and label propagation
10.1007/s00779-022-01688-8 · doi-reference
Federated learning with dynamics-aware loss for label noise
10.1016/j.eswa.2026.131523 · doi-reference
Federated learning for smart healthcare: A survey
10.1145/3501296 · doi-reference
Fed-NL: A federated learning approach to suppress noise in participant datasets to reduce communication rounds for convergence
10.1109/tmc.2025.3558874 · doi-reference
Federated semi-supervised learning with contrastive representations against noisy labels
10.1016/j.asoc.2025.113421 · doi-reference
A comprehensive survey of federated intrusion detection systems: Techniques, challenges and solutions
10.1016/j.cosrev.2024.100717 · doi-reference
Federated learning with non-IID data: A survey
10.1109/jiot.2024.3376548 · doi-reference
Vertical federated learning: Concepts, advances, and challenges
10.1109/tkde.2024.3352628 · doi-reference
A survey on federated learning systems: Vision, hype and reality for data privacy and protection
10.1109/tkde.2021.3124599 · doi-reference
Exploring feature selection with limited labels: A comprehensive survey of semi-supervised and unsupervised approaches
10.1109/tkde.2024.3397878 · doi-reference
Federated graph anomaly detection via contrastive self-supervised learning
10.1109/tnnls.2024.3414326 · doi-reference
FedAWR: An interactive federated active learning framework for air writing recognition
10.1109/tmc.2023.3320147 · doi-reference
Oh-FedRec: One-shot and heterogeneous vertical federated recommendation system
10.1109/tce.2025.3532724 · doi-reference
10.1007/978-981-19-7083-2
10.1007/978-981-19-7083-2 · doi-reference
A comprehensive survey on communication-efficient federated learning in mobile edge environments
10.1109/comst.2025.3535957 · doi-reference