pFedDHPO: A Differentiable Approach for Personalized Hyperparameter Optimization in Federated Learning
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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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) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.003 | 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".