Scheduling and Resource Allocation for Federated Learning in Vehicular Networks
Bibliographic record
Abstract
In federated learning (FL), clients update their local machine learning models using private data that is not to be shared with others. In each update period, the local models are then shared with a central server that maintains a global model that is used by all the clients. In this paper we consider the problem of scheduling and bandwidth assignment for vehicles that share a wireless communication channel during the FL. The objective is to minimize the update period duration so that global model updates can occur as quickly as possible. This is done by creating a transmission schedule and a fractional bandwidth assignment for each FL update period. The problem is modeled as a mixed-integer nonlinear program (MINLP) and since the problem is NP-complete, approximation algorithms are introduced that yield near-optimal solutions. This is done by doing a binary search on the update duration using a fractional relaxation and then by applying different dependent rounding procedures to obtain valid solutions. A variety of simulation results are presented that demonstrate the excellent performance of the proposed solutions when compared to the results obtained by an optimum direct solver on the same inputs.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".