Distributed Learning on Mobile Devices: A New Approach to Data Mining in the Internet of Things
Bibliographic record
Abstract
It is well known that deep learning is one of the most important methods for data mining. With the development of the fifth-generation mobile networks (5G) and the Internet of Things (IoT), the large volume of data collected in IoTs provides a new way to improve the capability of deep learning. Due to privacy, bandwidth, and legal concerns, it is impractical to send the data to a server or the cloud. The computing power of mobile devices makes it possible to process the data. Therefore, this article focuses on training these models in mobile devices. To solve the challenges, including unreliable networks, constrained resources, and slow convergence, we let multiple mobile devices learn a shared model collaboratively. We propose a novel architecture, GREAT, where each node chooses partners to share local model parameters according to link reliability. To balance the constrained resources and learning effectiveness, an optimization problem is developed by taking the reliability threshold as the variable of controlling the resources’ overhead. To implement this architecture, a dynamic control algorithm called Alpha-GossipSGD has been proposed. Its performance is evaluated by extensive experiments, which show that Alpha-GossipSGD can realize stable learning effectiveness over unreliable networks with constrained resources.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| 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".