Research map / Federated learning and privacy / Personalized and heterogeneous FL
Federated Patient Hashing
Read the paperCheck your idea against 108,967 papers
AI-extracted summary. Written by AI from the paper's title only (no summary yet). Numbers and claims can be wrong or missing context, so check the paper before you cite or build on it.
Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| federated hashing (FPHcen) vs centralized-data MLP baseline | FPH · AKI case, 64 bits | MAP (%): 54.58 -> 53.26 | No clear effect |
| FPH (centralized) vs best non-neural hashing SCM | FPHcen · AKI stage, 16 bits | MAP (%): 45.37 -> 48.30 | Helped |
| FPH (centralized) vs SCM on AKI case | FPHcen · AKI case, 64 bits | MAP (%): 53.67 -> 53.26 | No clear effect |
| decentralized (one pass) vs centralized training (ablation) | FPH · AKI stage, 64 bits | MAP (%): 48.55 -> 48.72 | No clear effect |
| train decentralized model to convergence (multiple passes) instead of one pass (ablation) | FPHde · AKI stage precision | precision | Helped |
| use only a subset (5 of 20) of institutions' data (ablation) | FPH · AKI case/stage precision | precision | No clear effect |
Closest papers by meaning
- dFLMoE: Decentralized Federated Learning via Mixture of Experts for Medical Data Analysis (CVPR 2025 · similarity 0.84)
- Learning Together Securely: Prototype-Based Federated Multi-Modal Hashing for Safe and Efficient Multi-Modal Retrieval (AAAI 2025 · similarity 0.84)
- FedVCK: Non-IID Robust and Communication-Efficient Federated Learning via Valuable Condensed Knowledge for Medical Image Analysis (AAAI 2025 · similarity 0.84)
- Private Heterogeneous Federated Learning Without a Trusted Server Revisited: Error-Optimal and Communication-Efficient Algorithms for Convex Losses (ICML 2024 · similarity 0.83)
- MH-pFLID: Model Heterogeneous personalized Federated Learning via Injection and Distillation for Medical Data Analysis (ICML 2024 · similarity 0.83)
- FedPIA – Permuting and Integrating Adapters Leveraging Wasserstein Barycenters for Finetuning Foundation Models in Multi-Modal Federated Learning (AAAI 2025 · similarity 0.83)
Writing a paper in this area? scime draft is a free LaTeX editor with AI included, real-time co-authors and a check of your references and venue rules before you submit. It also imports OpenAI Prism projects.