FedAR: Federated Artificial Resampling for Imbalanced Facial Emotion Recognition
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Federated Learning (FL) has emerged as an essential tool for computing devices to participate in collaborative training of deep learning models. However, due to the decentralized distribution of data over clients/local computing devices, the class imbalance problem has become evident, causing severe degradation in the performance of the global model. Motivated by the emergence of FL models in emotion recognition, the current study proposes an FL-based facial emotion recognition system by addressing local imbalance data problems encountered in client devices. First, the local imbalance problem is mitigated by utilizing the data-level artificial resampling method on the client side. To address the possibility of an adversarial attack using imbalanced data, the local training is equipped with a pre-training check to verify if the data being used is imbalanced above a predefined threshold of imbalance ratio. In case of high imbalance, a pre-training step will balance the data locally without sharing any information with other participants thereby ensuring privacy in the FL framework. Experiments have been conducted by using benchmark facial emotion recognition data with a balanced testing strategy. It indicated that considerable improvement can be achieved by the proposed FL-based facial emotion recognition model.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 it