Incentive Mechanism Design for Cross-Device Federated Learning: A Reinforcement Auction Approach
Bibliographic record
Abstract
In the operational context of a cross-device federated learning (FL), the efficient allocation of resources, such as transmission powers, channels, and computation resources, significantly impacts overall performance. Existing research in cross-device FL has predominantly concentrated on either resource allocation to enhance training accuracy or incentivizing participation, while ignoring their integrated designs for further improving the performance in cross-device FL. Different from existing work, in this paper, we jointly integrate the power allocation, channel assignment, user selection, and allocation of computation frequency into the design of incentive mechanism, where each mobile user plays a dual role as both a buyer and a seller. Because of complex resource allocation, truthfulness guarantee in a dual role scenario, and unavailable prior information, the considered mechanism design problem is challenging. To tackle such combinatorial problem, we propose a Reinforcement Auction Mechanism (RAM), comprising two layers. The upper layer features a Hybrid Action Reinforcement Learning scheme to learn the outcomes of user selection and payments. In the lower layer, each selected mobile user optimizes its resources to maximize its utility. Theoretical analyses affirm that our proposed RAM ensures individual rationality and truthfulness. Extensive simulations have been conducted to validate the effectiveness of the proposed RAM.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".