A Novel Reputation-aware Client Selection Scheme for Federated Learning within Mobile Environments
Bibliographic record
Abstract
This paper studies the problem of training federated deep learning models over a mobile environment. Stemming from the federated learning (FL) concept, deep learning models on mobile devices can be trained for various use cases including but not limited to image sorting and prediction of upcoming words. Mobile devices have access to rich data sets through embedded sensors and as well as installed software, and these feature rich data can facilitate solid training models, including personal images and other behaviometric features. However, utilizing the data through conventional approaches can potentially lead to privacy leakages. In this paper, we propose an alternate strategy that builds on the Federated Learning (FL) concept, to keep the training data on distributed mobile devices, and train a shared model by aggregating updated local models. The contribution of this study is an optimal user selection method for the federated learning environment based on reputation scores. Through extensive validation experiments considering two different model architectures and three datasets, our experiments show that the proposed approach is stable over data that is not independent nor identically distributed (i.e., non-IID) and under imbalanced distribution. Experimental results show that the proposed reputation-aware FL scheme can achieve improvements in the test accuracy up to 9.30% under different data sets.
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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 | 0.005 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.005 |
| Open science | 0.005 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| 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".