Privacy-Utility Trade-offs in Federated Learning for 6G Networks: A Systematic Evaluation of Software-based Privacy Mechanisms
Bibliographic record
Abstract
Federated Learning (FL) enhances privacy by training models locally, but remains vulnerable to inference attacks. We systematically evaluate software-based privacy mechanisms in FL for 6G networks, comparing Homomorphic Encryption (HE), Differential Privacy (DP), and hybrid approaches. We implemented five configurations including our novel Sequential Hybrid that temporally separates DP (training phase) and HE (aggregation phase), unlike existing simultaneous methods. Evaluations on CIFAR-10 revealed clear privacy-utility trade-offs: stronger privacy mechanisms progressively reduced accuracy while improving protection. Our sequential approach outperformed simultaneous application in learning stability, while HE demonstrated superior resilience to data heterogeneity compared to hybrid methods under non-IID conditions. This systematic evaluation provides empirical guidance for privacy mechanism selection in 6G networks requiring both strong privacy and robust performance across heterogeneous devices.
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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 | 0.015 | 0.039 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.006 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| 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".