Nested Quasi-Newton Optimization for Federated Learning Under Periodic Deterministic Communication Constraints
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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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".