pFedDHPO: A Differentiable Approach for Personalized Hyperparameter Optimization in Federated Learning
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Hyperparameter optimization (HPO) is crucial for federated learning (FL) performance. Given the inherent data heterogeneity across clients, recent research has focused on providing personalized hyperparameters for individual clients. However, such personalized approaches introduce exponential search complexity as the number of clients increases, significantly reducing the efficiency of existing HPO methods. To address this challenge, we propose pFedDHPO, a novel personalized HPO framework that efficiently optimizes hyperparameters in a differentiable manner. Specifically, pFedDHPO formulates personalized HPO as an optimization problem targeting joint distribution parameters within the clients’ search space and leverages gradient information from differentiable validation loss to substantially enhance the efficiency of the HPO process. Experimental results demonstrate that pFedDHPO achieves state-of-the-art performance compared to baseline methods, improving accuracy by up to 18.35% under extreme Non-IID data distributions. Additionally, the framework reduces communication overhead by 41.2% compared to conventional HPO methods, making it highly scalable for resource-constrained FL deployments.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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