Stochastic Device Scheduling and Power Control in Federated Learning with Energy Harvesting
Bibliographic record
Abstract
Federated Learning (FL) is emerging as a promising approach for enabling collaborative machine learning without centralizing data, particularly within Internet of Things (IoT). However, optimizing FL efficiently in dynamic environments under delay and energy constraints is challenging due to factors such as device heterogeneity and various connection quality. Moreover, the energy harvesting capabilities of many IoT devices add a new dimension to optimizing FL in wireless networks, a topic that remains underexplored. In this paper, we formulate an optimization problem to balance the trade-off between maximizing device scheduling probability and minimizing communication overhead. By leveraging Lyapunov optimization, we transform the original objective into a stability problem. We then introduce Sto-POLISH, a stochastic power control and device scheduling algorithm, that dynamically selects optimal power and scheduling probabilities while satisfying three long-term constraints. Simulations demonstrate that Sto-POLISH shows great performance in achieving fast convergence, reducing battery energy consumption, and maintaining communication overhead within acceptable limits compared to existing benchmarks.
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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 | 0.000 | 0.000 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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; a candidate call from one teacher head, 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".