An Efficient and Privacy-Preserving AdaBoost Federated Learning Framework for AiP System
Bibliographic record
Abstract
As the global population continues to age rapidly, Aging in Place (AiP) solutions have become increasingly vital for enabling elderly individuals to maintain their independence and continue living comfortably in their own homes. These solutions leverage advanced technologies such as smart homes and remote health monitoring. However, in real-world AiP applications, the health data needed for accurate predictions is often spread across multiple medical institutions, which raises signficant privacy concerns when integrating and analyzing the data. To address this challenge, we propose an efficient and privacy-preserving AdaBoost learning framework for vertically partitioned AiP data by utilizing Symmetric Homomorphic Encryption (SHE) technique. To ensure compatibility with the integer-based constraints of SHE, we adopt a straightforward weight quantization strategy by representing AdaBoost sample weights as integers. This design simplifies encrypted computation and maintains the boosting mechanism’s effectiveness. Our theoretical and experimental evaluations validate both the accuracy and security of the proposed framework, highlighting its practical viability for deployment in real-world AiP systems.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".