DANCE: Distributed Generative Adversarial Networks with Communication Compression
Bibliographic record
Abstract
Generative adversarial networks (GANs) have shown great success in deep representations learning, data generation, and security enhancement. With the development of the Internet of Things, 5th generation wireless systems (5G), and other technologies, the large volume of data collected at the edge of networks provides a new way to improve the capabilities of GANs. Due to privacy, bandwidth, and legal constraints, it is not appropriate to upload all the data to the cloud or servers for processing. Therefore, this article focuses on deploying and training GANs at the edge rather than converging edge data to the central node. To address this problem, we designed a novel distributed learning architecture for GANs, called DANCE. DANCE can adaptively perform communication compression based on the available bandwidth, while supporting both data and model parallelism training of GANs. In addition, inspired by the gossip mechanism and Stackelberg game, a compatible algorithm, AC-GAN is proposed. The theoretical analysis guarantees the convergence of the model and the existence of approximate equilibrium in AC-GAN. Both simulation and prototype system experiments show that AC-GAN can achieve better training effectiveness with less communication overhead than the SOTA algorithms, i.e., FL-GAN and MD-GAN.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".