Layer-wise Federated Learning for Mobile Networks
Bibliographic record
Abstract
Data privacy concerns have become a significant challenge in modern communication networks, where network clients generate diverse types of potentially sensitive information. Federated learning (FL) emerges as a privacy-preserving solution that allows distributed model training without centralizing sensitive data, thus enabling collaborative learning across decentralized networks. To collaboratively learn a global model in FL, participating users and a central entity periodically exchange information. Real-world network conditions, particularly varying channel quality, present obstacles to effective information exchange in federated learning implementations. In this paper, we introduce a federated learning framework that takes into account the channel quality variations among users and enhances resource utilization with reduced compromise to model predictive performance. Our proposed framework capitalizes on the observation that neural network layers exhibit diverse levels of sensitivity to noise. Our approach leverages this observation by selectively transmitting per-layer model updates based on channel conditions. This strategy not only protects critical model components from adverse channel effects but also conserves transmission resources. We demonstrate that our method achieves comparable accuracy to traditional approaches while significantly reducing communication overhead. To showcase its potential, we validate our method using two datasets. Our simulation results demonstrate promising performance in terms of both resource utilization and accuracy.
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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 | 0.003 | 0.007 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 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".