Nested Quasi-Newton Optimization for Federated Learning Under Periodic Deterministic Communication Constraints
Why this work is in the frame
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Bibliographic record
Abstract
Federated Learning (FL) enables decentralized model training while preserving data privacy, however, real-world deployments are often constrained by Periodic Deterministic Communication (PDC) schedules, where communication between clients and the central server occurs at fixed intervals due to bandwidth limitations, energy constraints, or regulatory restrictions. These rigid schedules introduce fundamental challenges, including delayed model updates, model drift, inefficient convergence, and heightened sensitivity to non-IID data distributions, which undermine FL performance in practical settings. To address these limitations, we propose Federated Nested Quasi-Newton Optimization (FedNQN), a novel framework that accelerates convergence and enhances FL robustness under PDC constraints. FedNQN integrates curvature-aware central acceleration with variance-controlled local adaptation, ensuring stable learning dynamics despite restricted communication. At the global level, second-order curvature information accelerates model updates, compensating for infrequent synchronization, while local updates leverage variance-controlled optimizations to mitigate drift and adapt to heterogeneous data distributions. This coordinated optimization strategy enhances convergence speed, improves model accuracy, and maintains computational efficiency, making FL more adaptable to real-world constraints. Extensive experiments on benchmark datasets validate FedNQN’s effectiveness, demonstrating superior performance over state-of-the-art FL methods in terms of stability, scalability, and resilience to communication inefficiencies.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.009 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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