Federated Constrastive Learning and Visual Transformers for Personal Recommendation
Why this work is in the frame
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Bibliographic record
Abstract
Abstract This paper introduces a novel solution for personal recommendation in consumer electronic applications. It addresses, on the one hand, the data confidentiality during the training, by exploring federated learning and trusted authority mechanisms. On the other hand, it deals with data quantity, and quality by exploring both transformers and consumer clustering. The process starts by clustering the consumers into similar clusters using contrastive learning and k-means algorithm. The local model of each consumer is trained on the local data. The local models of the consumers with the clustering information are then sent to the server, where integrity verification is performed by a trusted authority. Instead of traditional federated learning solutions, two kinds of aggregation are performed. The first one is the aggregation of all models of the consumers to derive the global model. The second one is the aggregation of the models of each cluster to derive a local model of similar consumers. Both models are sent to the consumers, where each consumer decides which appropriate model might be used for personal recommendation. Robust experiments have been carried out to demonstrate the applicability of the method using MovieLens-1M, and Amazon-book. The results reveal the superiority of the proposed method compared to the baseline methods, where it reaches an average accuracy of 0.27, against the other methods that do not exceed 0.25.
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Direct model labels (unvalidated)
Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.
| Model arm | Categories | Study design | Confidence |
|---|---|---|---|
| gemma | no category Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Simulation or modeling | low |
| gpt | no category Domain: not available · Genre: Methods About the Canadian research system: no · About a Canadian topic: no | Other design | low |
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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