ArticleNature communications2023
Decentralized federated learning through proxy model sharing.
Article in Nature communications, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 21 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
21 citing papers in PubMed.
- Hybrid deep reconstruction for vignetting-free upconversion imaging through scattering in epsilon-near-zero materials.Light, science & applications · 2026Article
- Fully automated system predicts osteoporotic vertebral fracture across institutions using lumbar MRI paraspinal muscle signatures.NPJ digital medicine · 2026Article
- Bioinformatics and artificial intelligence in genomic data analysis: current advances and future directions.Molecular genetics and genomics : MGG · 2025Review
- Simulating Federated Learning to Enable Multi-Hospital Collaboration for Lumbopelvic Alignment Estimation.JOR spine · 2025Article
- Optimizing Client Participation in Communication-Constrained Federated LLM Adaptation with LoRA.Sensors (Basel, Switzerland) · 2025Article
- Revolutionizing gastrointestinal cancer research with artificial intelligence: From precision patient stratification to real-world evidence.World journal of gastrointestinal oncology · 2025Review
- Collaborative and privacy-preserving cross-vendor united diagnostic imaging via server-rotating federated machine learning.Communications engineering · 2025Article
- Privacy-preserving Federated Learning and Uncertainty Quantification in Medical Imaging.Radiology. Artificial intelligence · 2025Review
- Decentralized Personalization for Federated Medical Image Segmentation via Gossip Contrastive Mutual Learning.IEEE transactions on medical imaging · 2025Article
- General lightweight framework for vision foundation model supporting multi-task and multi-center medical image analysis.Nature communications · 2025Article
- A meta-learning-based robust federated learning for diagnosing lung adenocarcinoma and tuberculosis granulomas.Frontiers in oncology · 2025Article
- Revolutionizing healthcare data analytics with federated learning: A comprehensive survey of applications, systems, and future directions.Computational and structural biotechnology journal · 2025Review
- A polynomial proxy model approach to verifiable decentralized federated learning.Scientific reports · 2024Article
- Introducing edge intelligence to smart meters via federated split learning.Nature communications · 2024Article
- An international study presenting a federated learning AI platform for pediatric brain tumors.Nature communications · 2024Article
- Differentially Private Client Selection and Resource Allocation in Federated Learning for Medical Applications Using Graph Neural Networks.Sensors (Basel, Switzerland) · 2024Article
- Real-World-Time Data and RCT Synergy: Advancing Personalized Medicine and Sarcoma Care through Digital Innovation.Cancers · 2024Review
- Secure Aggregation Protocol Based on DC-Nets and Secret Sharing for Decentralized Federated Learning.Sensors (Basel, Switzerland) · 2024Article
- Decentralized federated learning through proxy model sharing.Nature communications · 2023Article
- Federated learning enables big data for rare cancer boundary detection.Nature communications · 2022Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Institutions in highly regulated domains such as finance and healthcare often have restrictive rules around data sharing. Federated learning is a distributed learning framework that enables multi-institutional collaborations on decentralized data with improved protection for each collaborator's data privacy. In this paper, we propose a communication-efficient scheme for decentralized federated learning called ProxyFL, or proxy-based federated learning. Each participant in ProxyFL maintains two models, a private model, and a publicly shared proxy model designed to protect the participant's privacy. Proxy models allow efficient information exchange among participants without the need of a centralized server. The proposed method eliminates a significant limitation of canonical federated learning by allowing model heterogeneity; each participant can have a private model with any architecture. Furthermore, our protocol for communication by proxy leads to stronger privacy guarantees using differential privacy analysis. Experiments on popular image datasets, and a cancer diagnostic problem using high-quality gigapixel histology whole slide images, show that ProxyFL can outperform existing alternatives with much less communication overhead and stronger privacy.
Identifiers
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.