Cross-Device Distributed Federated Learning Coalition Formation Game for Constrained IoT
Bibliographic record
Abstract
Edge computing is an efficient way to help constrained IoT devices by offloading heavy tasks on edge servers, especially computing tasks related to Machine Learning (ML). Moreover, such devices can only store a limited amount of data because of their reduced capacity. Consequently, ML is bound to be smeared with relatively high error prediction as these devices resort to a small training dataset for their learning. To mend that issue, IoT devices can group in clusters and resort to Federated Learning (FL) with their pairs in the same cluster or coalition. However, the learned model needs to be transmitted repeatedly over a wireless access network, which is energy consuming. Hence, although learning collectively through FL can reduce the learned model variance, it inflicts a communication cost, dependent on the coalition size, that must be taken into account. Therefore, a cost function is devised astutely by factoring in both the prediction error and communication cost in a learning cluster. Then, a coalition formation game is conceived to minimize the devised cost function. Autonomous IoT devices will engage in the proposed game leading to coalitions of optimal size. Once clusters are formed, distributed FL is applied in any cluster in order to reduce the learning error of participating devices while curbing their communication cost.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".