Federated Learning Game in IoT Edge Computing
Bibliographic record
Abstract
Edge Computing provides an effective solution for relieving IoT devices from the burden of handling Machine Learning (ML) tasks. Further, given the limited storage capacity of these devices, they can only accommodate a restricted amount of data for training, resulting in higher error rates for ML predictions. To address this limitation, IoT devices can leverage Edge Computing and collaborate in the learning process through a designated peer acting as an Edge device. However, the transmission of offloaded tasks over a wireless access network poses challenges in terms of time and energy consumption. Consequently, although collaborative learning can diminish the variance of the learned model, it introduces a communication cost, dependent on the chosen Edge device. In light of these considerations, this paper introduces a coalition formation game that proposes a distributed Federated Learning approach, where devices autonomously and efficiently select the most suitable Edge device, aiming to minimize both their learning error and communication cost.
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How this classification was reachedexpand
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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.019 | 0.038 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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; both teacher heads agree on what is shown here.
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".