Adaptive Ratio-Based-Threshold Gradient Sparsification Scheme for Federated Learning
Bibliographic record
Abstract
Federated learning (FL) is a distributed learning paradigm that has received great attention over the past several years due to its privacy-preserving property. As the models involved in FL are usually dense and overparameterized however, various studies are being conducted in gradient sparsification to reduce the high communication overhead. While many of the recently-presented schemes that are variations of top-k have shown competitive inference accuracy convergence to the baseline “vanilla” FL, they have a fixed sparsity rate throughout all of the communication rounds, which leads to an unnecessary excessive transmission of gradients as the global model converges. Furthermore, the constant-threshold gradient sparsification method called Threshold-z-, that is well-known for its dynamic rate, does not account for the ratio between the gradient and the pre-update parameter value, causing some gradients that are orders of magnitude larger than the pre-update parameter values to be neglected in the following aggregation process. In this paper, we introduce a new algorithm that addresses both of these issues, called adaptive ratio-based-threshold gradient sparsification method. Our main idea is introducing a custom gradient sparsity threshold for each local parameter based on their pre-update value and a hyperparameter denoted as Ψ. We demonstrate through image classification experiments on MNIST and CIFAR-10 datasets in both independent-and-identically-distributed (IID) and non-IID settings that under optimal Ψ, the gradient sparsity rates adapt & increase as the global model converges, while simultaneously producing inference accuracies that are competitive to vanilla FL.
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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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| 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".