Federated Learning on Knowledge Graph Embeddings via Contrastive Alignment
Bibliographic record
Abstract
In conventional federated learning (FL) frameworks for knowledge graph embedding (KGE), individual clients independently train their local KGE models. A trusted server then collects and aggregates the locally computed embeddings (e.g., by averaging) to generate a consolidated, shared model. This process maintains data privacy throughout FL training, as the server does not require direct access to client data. However, data heterogeneity (i.e., non-identically distributed data across clients) significantly challenges the performance of FL global averaging-based aggregation algorithms, where averaging embeddings can lead to oversmoothing and loss of relational patterns among entities. To address these challenges, we introduce a supervised, KGE model-agnostic contrastive learning (CL) approach for federated settings. Our approach uses CL to align embeddings of the same entity across clients while maintaining distinctions between different entities, thus preserving both intra-and inter-entity relationships during aggregation. Experiments on benchmark datasets demonstrate that our proposed model outperforms state-of-the-art FL-KGE aggregation algorithms, particularly with large numbers of clients.
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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 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.006 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.002 | 0.003 |
| 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".