Convergence Acceleration for Knowledge Distillation-Enabled Wireless Federated Learning
Bibliographic record
Abstract
Federated distillation (FD), which inherits the privacy-preserving nature of federated learning (FL), has recently attracted increasing attention due to its communication efficiency in training the global model through logits aggregation. However, FD performance suffers from slow convergence due to statistically heterogeneous data and failures in logits transmission from resource-constrained clients over unreliable wireless links. Client selection and efficient resource allocation are typical methods to speed up the convergence in FD. However, most existing works have not considered the inherent inter-dependencies between these two methods. To address this issue, in this paper, we propose a Stackelberg game-based framework to optimally balance client selection and resource allocation. Specifically, client selection is formulated as the leader-level problem to reduce the number of required communication rounds. Subsequently, resource allocation is formulated as the follower-level problem to maximize their successful uploading rates in each round. By decomposing the follower-level problem into three subproblems, the closed-form solutions of transmission power, computation frequency, and the number of uploaded logits allocations are derived through monotonicity analysis. To solve the leader-level problem, we first derive the upper bound of the convergence of the FD global loss. Based on this, an uncertainty-based client selection scheme and an attention-based logits sampling method are proposed to optimally solve the leader’s optimization problem. Finally, the Stackelberg equilibrium is reached when all selected clients can successfully upload logits to the server. Simulation results demonstrate that the proposed Stackelberg equilibrium solutions significantly enhance the global model convergence speed.
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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 | 0.008 |
| 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 | 0.009 | 0.005 |
| 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; 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".