Privacy-Preserving Heterogeneous Personalized Federated Learning With Knowledge
Bibliographic record
Abstract
Personalized federated learning (PFL) has gained increasing attention due to its success in handling the statistical heterogeneity of participants' local data by building distinct local models for participants. However, existing PFL schemes require the identical architecture and size of participants' models, e.g., the same number of layers in convolutional neural networks (CNN). In addition, the growing privacy issues (e.g., local update leakage to the curious server in model aggregation) have not been resolved in PFL. The utilization of identical model architectures among participants reduces the cost of privacy attacks since only one uniform attack method is required to extract private information, exacerbating the privacy threat. This paper proposes a novel privacy-preserving PFL framework that supports heterogeneous model architectures and sizes in delivering personalized models for different participants. Specifically, we utilize participants' knowledge, i.e., the soft predictions of local models on a public dataset, to effectively identify participants with similar data distributions regardless of the specific model architectures used. Based on the participants' knowledge, and their computing and storage capabilities, we employ the affinity propagation (AP) algorithm to implement a multi-level participant clustering mechanism for enabling heterogeneous PFL. Since knowledge is independent of original data, it is considered privacy-preserving during the clustering process. We also devise the ring aggregation algorithm to guarantee participants' privacy during the federated training process. In this way, each participant benefits from other participants with similar data distributions privately and obtains a satisfying personalized model. Furthermore, the cross-cluster knowledge transfer method boosts the personalization performance of weak participants. Sufficient theoretical analyses prove the effectiveness and privacy-preserving capacity of the proposed scheme. Extensive experiments on three benchmark datasets also demonstrate the superiority of our proposed scheme in various settings while maintaining privacy protection, outperforming other state-of-the-art schemes.
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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 | 0.005 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.003 | 0.007 |
| Open science | 0.004 | 0.007 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.001 | 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".