Communication-efficient Preference-based Federated Multi-resource Allocation
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Bibliographic record
Abstract
In many applications in communication networks, smart energy systems, edge computing, smart cities, etcetera, agents wish not to exchange information with other agents in the network and make decisions based on their choices and preferences and local computation. The agents cooperate with a central server to minimize the overall cost to the network. Such a setting is called a federated setting; it has recently received much interest from the research community. In this paper, we develop a communication-efficient stochastic multi-resource allocation algorithm for federating settings, generalizing the additive increase multiplicative decrease (AIMD) algorithm. Our solution does not require inter-agent communication. We consider a central server that coordinates with the agents to track the aggregate consumption of resources and sends one-bit feedback signals in the network when the resource capacity constraints are violated. We show the convergence of the average allocations to the optimal values through numerical results.
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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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.001 |
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 it