Fed-EHP: Efficient and Heterogeneous Privacy-Preserving Personalized 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
Personalized federated learning (pFL) has emerged as a promising paradigm for mitigating client heterogeneity in distributed machine learning. In cross-device scenarios, however, the continuous generation of sensitive data by clients introduces severe communication bottlenecks and privacy risks, limiting the effectiveness of existing pFL methods. To ad dress these challenges, we propose Fed-EHP, a novel privacy preserving and communication-efficient framework for hetero geneous pFL. The originality of Fed-EHP lies in its task-specific synergistic integration of three customized components within a unified fog-assisted architecture: 1) Data-aware client clustering at the fog layer to alleviate statistical heterogeneity and reduce communication load; 2) MIFE-based secure aggregation to ensure strong privacy protection against inference attacks while preserving model utility; and 3) Cluster-driven personalized knowledge distillation to effectively address model heterogeneity and boost personalization across devices and fog nodes. To demonstrate privacy guarantee and security of the proposed framework, we provide a formal security analysis. We also con duct extensive experiments on MNIST, Fashion-MNIST, CIFAR 10, and CIFAR-100. Fed-EHP consistently delivers notable im provements in both accuracy and communication efficiency over state-of-the-art pFL methods. These results demonstrate that our integrated and customized framework enables capabilities and performance gains that are unattainable using existing techniques in isolation, establishing Fed-EHP as a practical and reliable solution for real-world heterogeneous federated learning.
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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 | 0.002 |
| 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 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.008 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| 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