Model Pruning for Efficient Over-the-Air Federated Learning in Tactical Networks
Bibliographic record
Abstract
This paper considers federated learning (FL) in a tactical network (TN), by adopting computationally efficient over-the-air aggregation (OTA) to train a global model at a parameter server (PS). Memory limited edge devices ideally require to store a deep neural network (DNN) model that is compact in size but has high accuracy. This work explores the effects on the accuracy of the model due to the compression technique of pruning, as the model parameters are aggregated OTA. Further, we quantify the effects of Rayleigh fading and additive white Gaussian noise (AWGN) on the accuracy of the DNN model, at different signal-to-noise ratios (SNRs), exploring the size-accuracy-SNR trade-off for both uncompressed and pruned versions of the DNN model. Simulation results, particularly at high SNR, show little difference in accuracy among the uncompressed, 30%, and 50% pruned models, but the sizes of the pruned models are reduced roughly by 0.23x and 0.40x than that of uncompressed model's size, respectively. Pruning the model to 70% results in 0.58x reduction in size but the cost is significant lowering of accuracy, even at high SNR. Depending upon the availability of resources at edge devices, size-accuracy-SNR trade-offs can be exploited as various overlapping trends are obtained in the simulation results.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".