Bibliographic record
Abstract
Compared to conventional two-layer federated learning (FL), three-layer FL, which adds a layer of edge servers between the central server and clients, is less studied but could provide better training performance in terms of reducing elapsed wall-clock time to complete training. However, three-layer FL inherits and magnifies some inherent challenges in two-layer FL, such as client heterogeneity. With different computing capabilities and network connections, slow clients require exceedingly long training times. To alleviate the negative effect due to slow clients, this paper is the first to study asynchronous three-layer FL, where edge servers conduct local aggregation without waiting for slow clients and the central server aggregates updates from fast edge servers. Unfortunately, asynchronous three-layer FL could suffer from performance degradation as when aggregating updates from slow clients and edge servers, those updates are not computed based on the newest global model. Therefore, we propose a staleness-aware framework, Pegasus, with newly designed client selection, compression, and update aggregation mechanisms to improve every important aspect during training. Our extensive evaluation of different training tasks demonstrates that Pegasus can achieve a reduction in elapsed wall-clock training time by at least 46.8% with an increase in converged accuracy of the trained global model by up to 0.3% as compared to the state-of-the-art.
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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".