Probabilistic Client Sampling and Power Allocation for Wireless Federated Learning
Bibliographic record
Abstract
Despite the many known benefits of Federated Learning (FL), in the wireless environment, its performance is significantly impacted by the statistical and system heterogeneities among the local data sets and local clients. Therefore, judicious sampling of clients and resource allocation among them are of vital importance in FL. In this work, we consider the online joint optimization of probabilistic client sampling and power allocation to improve the training performance of wireless FL. Our optimization is based on a new convergence bound for non-convex loss functions under probabilistic client sampling, which considers the different data ratios and gradient norms among clients. We propose a new algorithm based on the Lyapunov optimization framework, termed PCSPA, that accounts for how the statistical and system heterogeneities affect both the convergence rate and training time of FL, as well as the long-term power constraints and the expected number of sampled clients. Experiments on image classification with wireless FL show that the proposed algorithm can substantially outperform conventional separate optimization strategies and a state-of-the-art joint optimization method.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.002 |
| 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".