Privacy-Preserving Federated Learning for Coverage Prediction
Bibliographic record
Abstract
In 5G cellular networks, Machine Learning (ML) can be exploited to predict if a user equipment (UE) is in the coverage area of a neighbouring cell. This could improve crucial cellular network functionalities, such as handovers, interference mitigation and carrier aggregation. In this paper, we study the enhancement of UEs’ privacy in a Differentially Private-Federated Learning (DP-FL) scheme relying on the sampled Gaussian mechanism, assuming honest-but-curious threat model. With this technique, the UE’s privacy is protected by perturbing the averaged updates conducted at the server; also, the usage of client subsampling results in an amplified privacy and a reduced overhead in terms of communication. We demonstrate that the models trained with our approach can achieve better privacy-utility tradeoff than previous works can. In addition, we conduct membership inference attack to study the factors that impact the empirical privacy protection to the training data. We make a novel observation that suggests that for coverage prediction task, larger datasets and/or smaller ML models would provide stronger empirical privacy protection to training data. Beyond the task we consider, this observation could be a useful insight for dataset curation or model architecture selection in other domains and warrants additional investigation.
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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 | 0.024 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.005 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".