Multi-frame Scheduling for Federated Learning over Energy-Efficient 6G Wireless Networks
Bibliographic record
Abstract
It is envisioned that data-driven distributed learning approaches such as federated learning (FL) will be a key enabler for 6G wireless networks. However, the deployment of FL over wireless networks suffers from considerable energy consumption on communications and computation, which is challenging to meet stringent energy-efficiency goals of future sustainable 6G networks. In this paper, we investigate the energy consumption of transmitting scheduling decisions for FL deployed over a wireless network where mobile devices upload their local model to a coordinator (6G base station) for computing a global machine learning (ML) model iteratively. We consider that the coordinators have stringent energy efficiency goals. Therefore, to reduce the energy consumption due to the deployment of FL, we propose a novel multi-frame framework for FL that enables the coordinator to schedule wireless devices in one global round by only sending scheduling decisions at the beginning of each global round and setting the coordinator’s transmission module to sleep mode to save power. In particular, we formulate a mixed-integer non-linear problem (MINLP) to minimize the average collection time of all device’s local models by considering transmission errors. Then, we provide a novel method to solve the MINLP approximately and schedule wireless devices and allocate network resources. We demonstrate that our framework can save about 15 to 20 percent in some specific settings. Simulation results also show that our proposed algorithm outperforms traditional resource allocation methods and saves about 10% battery life per hundred global rounds in mobile device coordinators under certain scenarios.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".