Energy-Aware Structured Pruning Strategy for Scalable Federated Learning in IoT Networks
Bibliographic record
Abstract
The rapid growth of the Internet of Things (IoT) has led to a significant increase in connected devices, necessitating efficient and scalable solutions for data processing and analysis. Federated learning (FL) has emerged as a promising approach that enables collaborative training of machine learning (ML) models while preserving data privacy. However, the iterative nature of FL and the resource-constrained environment of edge devices present challenges in terms of energy efficiency, computational overhead, and communication noise. To address these issues, this work proposes an adaptive structured pruning strategy that dynamically prunes and regrows model filters based on the clients’ local accuracy thresholds. By adapting to client-specific communication conditions, such as channel noise and varying signal-to-noise ratios (SNR), the proposed method ensures robust performance and enhanced energy efficiency. Experimental results demonstrate significant energy savings of up to $30 \%$ and notable model size reductions while maintaining competitive accuracy. These findings validate the efficacy of the approach in optimizing FL for resource-constrained IoT ecosystems.
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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 | 0.002 | 0.005 |
| 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 | 0.001 |
| 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".