<i>Polaris:</i> Accelerating Asynchronous Federated Learning With Client Selection
Why this work is in the frame
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Bibliographic record
Abstract
Federated learning has garnered significant research attention as a privacy-preserving learning paradigm. Asynchronous federated learning has been proposed to improve scalability by accommodating slower clients, commonly referred to as stragglers. However, asynchronous federated learning suffers from slow convergence with respect to wall-clock time, due to the existence of data heterogeneity and staleness. Existing strategies struggled to tackle both difficulties for a wide range of deep learning models. To address the problem, we propose <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Polaris</i> , a theoretically sound design and a new take to client selection for asynchronous federated learning. With <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Polaris</i> , we first theoretically investigated the design space of client sampling strategies from a geometric optimization perspective, taking both data heterogeneity and staleness into account. Our design is not only theoretically proven, but also thoroughly tested in our reproducible experimental open-source testbed. Our experimental results demonstrates overwhelming evidence that <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Polaris</i> outperformed existing state-of-the-art client selection strategies by a substantial margin over a wide variety of tasks and datasets, as we train image classification models using CIFAR-10, CIFAR-100, CINIC-10, Federated EMNIST, and a language modeling model using the Tiny Shakespeare dataset. Further, our extensive array of ablation studies have also shown that <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Polaris</i> is both scalable and robust as the size of datasets scale up and data heterogeneity vary.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.004 | 0.000 |
| Research integrity | 0.000 | 0.002 |
| 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