{"total":31,"rows":[{"id":"2305.05230","title":"FedNoRo: Towards Noise-Robust Federated Learning by Addressing Class Imbalance and Label Noise Heterogeneity","created":"2023-05-09","cited_by_count":33,"area":null},{"id":"2205.03644","title":"Calibrating Label Distribution for Class-Imbalanced Barely-Supervised Knee Segmentation","created":"2022-05-07","cited_by_count":27,"area":null},{"id":"2303.14453","title":"Federated Learning without Full Labels: A Survey","created":"2023-03-25","cited_by_count":13,"area":null},{"id":"2307.05358","title":"Combating Data Imbalances in Federated Semi-supervised Learning with Dual Regulators","created":"2023-07-11","cited_by_count":9,"area":null},{"id":"2403.01387","title":"A Comprehensive Survey of Federated Transfer Learning: Challenges, Methods and Applications","created":"2024-03-03","cited_by_count":8,"area":null},{"id":"2305.00771","title":"Towards Unbiased Training in Federated Open-world Semi-supervised Learning","created":"2023-05-01","cited_by_count":7,"area":null},{"id":"2402.14430","title":"Robust Training of Federated Models with Extremely Label Deficiency","created":"2024-02-22","cited_by_count":7,"area":null},{"id":"2405.02081","title":"A Mutual Information Perspective on Federated Contrastive Learning","created":"2024-05-03","cited_by_count":7,"area":null},{"id":"2407.16560","title":"COALA: A Practical and Vision-Centric Federated Learning Platform","created":"2024-07-23","cited_by_count":7,"area":null},{"id":"2405.19789","title":"Estimating before Debiasing: A Bayesian Approach to Detaching Prior Bias in Federated Semi-Supervised Learning","created":"2024-05-30","cited_by_count":5,"area":null},{"id":"2406.18995","title":"FedMLP: Federated Multi-Label Medical Image Classification under Task Heterogeneity","created":"2024-06-27","cited_by_count":5,"area":null},{"id":"2410.23227","title":"(FL)$^2$: Overcoming Few Labels in Federated Semi-Supervised Learning","created":"2024-10-30","cited_by_count":5,"area":null},{"id":"2307.08809","title":"Local or Global: Selective Knowledge Assimilation for Federated Learning with Limited Labels","created":"2023-07-17","cited_by_count":4,"area":null},{"id":"2310.18815","title":"Rethinking Semi-Supervised Federated Learning: How to co-train fully-labeled and fully-unlabeled client imaging data","created":"2023-10-28","cited_by_count":4,"area":null},{"id":"2412.13757","title":"Federated Source-free Domain Adaptation for Classification: Weighted Cluster Aggregation for Unlabeled Data","created":"2024-12-18","cited_by_count":4,"area":null},{"id":"2211.08914","title":"Dual Class-Aware Contrastive Federated Semi-Supervised Learning","created":"2022-11-16","cited_by_count":3,"area":null},{"id":"2302.11823","title":"FedIL: Federated Incremental Learning from Decentralized Unlabeled Data with Convergence Analysis","created":"2023-02-23","cited_by_count":3,"area":null},{"id":"2303.11339","title":"FedMAE: Federated Self-Supervised Learning with One-Block Masked Auto-Encoder","created":"2023-03-20","cited_by_count":3,"area":null},{"id":"2307.02140","title":"Towards Open Federated Learning Platforms: Survey and Vision from Technical and Legal Perspectives","created":"2023-07-05","cited_by_count":3,"area":null},{"id":"2511.11778","title":"CATCHFed: Efficient Unlabeled Data Utilization for Semi-Supervised Federated Learning in Limited Labels Environments","created":"2025-11-14","cited_by_count":1,"area":null},{"id":"2604.20825","title":"FedSIR: Spectral Client Identification and Relabeling for Federated Learning with Noisy Labels","created":"2026-04-22","cited_by_count":1,"area":null},{"id":"2210.06434","title":"Cross-client Label Propagation for Transductive and Semi-Supervised Federated Learning","created":"2022-10-12","cited_by_count":0,"area":null},{"id":"2303.02445","title":"Federated Semi-Supervised Learning with Annotation Heterogeneity","created":"2023-03-04","cited_by_count":0,"area":null},{"id":"2407.10327","title":"Learning Unlabeled Clients Divergence for Federated Semi-Supervised Learning via Anchor Model Aggregation","created":"2024-07-14","cited_by_count":0,"area":null},{"id":"2505.21010","title":"Unified Alignment Protocol: Making Sense of the Unlabeled Data in New Domains","created":"2025-05-27","cited_by_count":0,"area":null},{"id":"2507.14181","title":"Semi-Supervised Federated Learning via Dual Contrastive Learning and Soft Labeling for Intelligent Fault Diagnosis","created":"2025-07-12","cited_by_count":0,"area":null},{"id":"2511.14302","title":"SAM-Fed: SAM-Guided Federated Semi-Supervised Learning for Medical Image Segmentation","created":"2025-11-18","cited_by_count":0,"area":null},{"id":"2602.21078","title":"ProxyFL: A Proxy-Guided Framework for Federated Semi-Supervised Learning","created":"2026-02-24","cited_by_count":0,"area":null},{"id":"2603.10341","title":"Federated Active Learning Under Extreme Non-IID and Global Class Imbalance","created":"2026-03-11","cited_by_count":0,"area":null},{"id":"2605.01810","title":"Federated Semi-Supervised Graph Neural Networks with Prototype-Guided Pseudo-Labeling for Privacy-Preserving Gestational Diabetes Mellitus Prediction","created":"2026-05-03","cited_by_count":0,"area":null},{"id":"2607.20641","title":"One Round Is All You Need: Analytic Federated Learning for Task-Heterogeneous Multi-Label Medical Image Classification","created":"2026-07-22","cited_by_count":0,"area":null}],"next_cursor":null}