Bibliographic record
Abstract
As a practical paradigm designed to involve large numbers of edge devices in distributed training of deep learning models, federated learning has witnessed a significant amount of research attention in the recent years. Yet, most existing mechanisms on federated learning assumed either fully synchronous or asynchronous communication strategies between clients and the federated learning server. Existing designs that were partially asynchronous in their communication were simple heuristics, and were evaluated using the number of communication rounds or updates required for convergence, rather than the wall-clock time in practice.In this paper, we seek to explore the entire design space between fully synchronous and asynchronous mechanisms of communication. Based on insights from our exploration, we propose Port, a new partially asynchronous mechanism designed to allow fast clients to aggregate asynchronously, yet without waiting excessively for the slower ones. In addition, Port is designed to adjust the aggregation weights based on both the staleness and divergence of model updates, with provable convergence guarantees. We have implemented Port and its leading competitors in Plato, an open-source scalable federated learning research framework designed from the ground up to emulate real-world scenarios. With respect to the wall-clock time it takes for converging to the target accuracy, Port outperformed its closest competitor, FedBuff, by up to 40% in our experiments.
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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 | 0.010 | 0.037 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.005 | 0.013 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.004 | 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".