Clustering-Based Custom Global Modules for Personalized Federated Learning
Bibliographic record
Abstract
The field of federated learning faces a fundamental challenge posed by the non-independent and identically distributed (Non-IID) data among heterogeneous clients. Personalized federated learning (PFL) addresses this issue by providing customized models for each client. The existing PFL methods ignore the impact of redundant information brought by shared knowledge on processing local tasks of clients, and clients prefer to find the mapping of local data distribution from shared knowledge space. This paper proposes a personalized aggregation method, FedCGM, which customizes a clustering coordinator for each client. The method extracts core knowledge from the global model that aligns with the local data distribution and adapts it to the local client. The challenge of FedCGM lies in identifying the mapping relationship between local data distributions and shared knowledge. To address this challenge, we construct posterior probability indices of the global modules on the target data distribution among participating members. This approach establishes a mapping relationship between data distribution and shared knowledge. We conducted extensive experiments on real-world datasets and found that FedCGM achieved higher accuracy compared to the state-of-the-art PFL methods.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.004 |
| Open science | 0.003 | 0.003 |
| 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".