FSSL-UC: Federated Semi-Supervised Learning Model With Uncertainty and Consistency
Bibliographic record
Abstract
Federated learning (FL) in practical applications frequently encounters the challenge of limited labeled data availability on client devices, where data annotation is often expensive or requires domain expertise. This scarcity, especially under non-independent and non-identically distributed (Non-IID) settings, leads to error accumulation from pseudo-labels and under-utilization of unlabeled data. To overcome this limitation, we propose a federated semi-supervised learning (FSSL) approach that jointly uses labeled and unlabeled data distributed across both server and clients. Building upon existing FSSL frameworks, our method introduces two key innovations: (1) an optimized client consistency regularization mechanism using Kullback-Leibler (KL) divergence for low-confidence samples, and (2) an advanced pseudo-labeling strategy incorporating Entropy Meaning Loss (EML) for high-confidence samples, which are designed to maximize the utility of unlabeled data and enhance model generalization capabilities. Our classification experiments on common (CIFAR-10, CIFAR-100, MNIST, SVHN) and medical datasets (Brain Tumor, Nail Disease) show that the proposed method outperforms the existing baseline models. On CIFAR-10, with only 10% labeled data (IID), the proposed FSSL-UC achieves a 1.33% higher accuracy than FedMatch, while under the more challenging setting of 1% labeled data (Non-IID), the improvement reaches 6.33%. On the brain tumor dataset, with 1% labeled data, FSSL-UC outperforms FedMatch by 14.47% (IID) and 7.61% (Non-IID). In addition, FSSL-UC also showed stable advantages on MNIST, SVHN, and Nail Disease datasets, demonstrating its robustness and generalization ability.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.007 | 0.014 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.005 | 0.003 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".