Blade: Pushing the Performance Envelope of Asynchronous Federated Learning
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Bibliographic record
Abstract
Asynchronous federated learning (FL) has been proposed to decrease the training time in conventional FL where the communication paradigm is synchronous. Instead of aggregating after receiving updates from all the selected clients, an asynchronous FL server conducts aggregation without waiting for slow clients. Though superior to synchronous FL, the performance of existing works in asynchronous FL — measured by the wall-clock time of global training — leaves much to be desired, as the staleness of client updates may degrade the performance substantially. In this paper, we propose Blade, a new stalenessaware framework that seeks to push the performance envelope of asynchronous FL by designing new mechanisms in all important design aspects of FL training, including client selection, adaptive pruning, quantization, and update aggregation. Blade selects clients based on their staleness and the quality of their previous updates. Before reporting to the server, every client prunes its update with a pruning amount related to its staleness and quantizes the pruned update. When aggregating updates, Blade tunes the aggregation weight of each update according to its staleness and divergence from the previous global model. In an extensive array of performance evaluations with six benchmark datasets, Blade consistently showed its substantial performance superiority over its state-of-the-art competitors. It decreased the wall-clock training time by up to 64.6%.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 it