Second-Order Wireless Federated Leaning via Nonparametric Hessian Estimation
Bibliographic record
Abstract
Quasi-Newton algorithms estimate the second-order information of loss landscape from its first-order information. They hence propose a promising solution for communication-efficient federated learning (FL), as they reduce the required number of training rounds while avoiding the necessity of exchanging local Hessians over the network. Despite that, the quasi-Newton approaches prove less effective in wireless FL, as the noisy aggregation in this case causes bias in the estimate of the Newton direction. This paper proposes a novel second-order wireless FL algorithm. The pivotal innovation lies in the server’s ability to estimate the global Hessian based on a window of noisy aggregations. The server acquires this ability by computing a stochastic estimator of the global Hessian under a Gaussian prior belief. Numerical experiments show that the proposed scheme can compute a less-biased estimator of the Newton direction, and hence a superior learning performance, as compared to the baseline.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".