Deep Compression for Efficient and Accelerated Over-the-Air Federated Learning
Bibliographic record
Abstract
Over-the-air federated learning (OTA-FL) is a distributed machine learning technique where multiple devices collaboratively train a shared model without sharing their raw data with a central server. The devices exchange model updates concurrently over-the-air and they are aggregated without the need for dedicated wireless resources for each device. A major challenge in OTA-FL is that edge devices are limited in their computation, energy, and communication resources. To address this, we investigate how deep neural network compression techniques can be applied in an OTA-FL system. We propose a compression pipeline comprised of pruning and quantization-aware training that significantly reduces both the computation and communication requirements while maintaining an on-par accuracy to the uncompressed models. We thoroughly investigate the reduction in model size, accuracy, and convergence when pruning and quantization-aware training are applied individually and collectively at multiple pruning and quantization levels. Detailed experiments are conducted on two-different deep learning models and two-different datasets, and under varying signal-to-noise ratios (SNRs) and numbers of clients. We then thoroughly present and discuss the resulting trade-offs and findings. Our results demonstrate that deep compression is very effective in an OTA-FL system and negligible losses in accuracy are possible while maintaining up to 80 percent reductions in model size.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.007 | 0.009 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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; both teacher heads agree on what is shown here.
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".