Minimizing Energy Consumption for Decentralized Federated Learning Using D2D Communications
Bibliographic record
Abstract
Federated learning (FL) is a promising distributed machine learning technique for building inference models over wireless networks due to its ability to maintain user privacy and reduce communication overhead. In this paper, we consider minimizing the energy consumption of a device-to-device (D2D) network while maintaining the convergence rate of FL subject to its time constraint. In the considered D2D network, each device has limited transmission range and is connected partially to other devices in the network. A group of devices can form a cluster and one of these devices is judiciously selected as a local aggregator (LA) to aggregate the local models of other devices in the cluster. Leveraging the nature of D2D communications, we exploit the devices that are located at the conflict zones of LAs. As such, the LAs can disseminate their local aggregated models among them. Towards this goal, a joint optimization problem, considering scheduling the devices to the LAs and computation frequency allocation of the devices, is presented. In order to solve this NP-hard problem, an iterative solution is devised. Particularly, we decompose it into two sub-problems, namely, LAs selection and device scheduling sub-problem and computation frequency allocation sub-problem. By solving theses sub-problems iteratively, a FedD2D (federated learning with D2D communications) scheme is proposed. MATLAB simulations are conducted to verify the effectiveness of the proposed FedD2D scheme over FL conventional schemes.
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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 | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.002 |
| 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".