GIFT: Toward Accurate and Efficient Federated Learning With Gradient-Instructed Frequency Tuning
Bibliographic record
Abstract
Federated learning (FL) enables distributed clients to collectively train a global model without revealing their private data, and for efficiency clients synchronize their gradients periodically. However, this can lead to the inaccuracy in model convergence due to inconsistent data distributions among clients. In this work, we find that there is a strong correlation between FL accuracy loss and the synchronization frequency, and seek to fine tune the synchronization frequency at training runtime to make FL accurate and also efficient. Specifically, aware that under the FL privacy requirement only gradients can be utilized for making frequency tuning decisions, we propose a novel metric called gradient consistency, which can effectively reflect the training status despite the instability of realistic FL scenarios. We further devise a feedback-driven algorithm called Gradient-Instructed Frequency Tuning (GIFT), which adaptively increases or decreases the synchronization frequency based on the gradient consistency metric. We have implemented GIFT in PyTorch, and large-scale evaluations show that it can improve FL accuracy by up to 10.7% with a time reduction of 58.1%.
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 | 0.003 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.003 |
| Open science | 0.003 | 0.003 |
| 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".