ArticleIEEE journal of biomedical and health informatics2022
Dynamic Neural Graphs Based Federated Reptile for Semi-Supervised Multi-Tasking in Healthcare Applications.
Article in IEEE journal of biomedical and health informatics, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
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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.
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Who cites it
8 citing papers in PubMed.
- A robust and personalized privacy-preserving approach for adaptive clustered federated distillation.Scientific reports · 2025Article
- Practical Implementation of Federated Learning for Detecting Backdoor Attacks in a Next-word Prediction Model.Scientific reports · 2025Article
- Recent methodological advances in federated learning for healthcare.Patterns (New York, N.Y.) · 2024Review
- Continuous patient state attention model for addressing irregularity in electronic health records.BMC medical informatics and decision making · 2024Article
- Data encoding for healthcare data democratization and information leakage prevention.Nature communications · 2024Article
- Federated Node Classification over Distributed Ego-Networks with Secure Contrastive Embedding Sharing.Proceedings of the ... ACM International Conference on Information & Knowledge Management. ACM International Conference on Information and Knowledge Management · 2024Article
- Privacy-Preserving Graph Machine Learning from Data to Computation: A Survey.SIGKDD explorations : newsletter of the Special Interest Group (SIG) on Knowledge Discovery & Data Mining · 2023Article
- Federated Learning in Health care Using Structured Medical Data.Advances in kidney disease and health · 2023Review
Corrections and comments
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Authors and funding
3 authors.
Funding
Abstract
AI healthcare applications rely on sensitive electronic healthcare records (EHRs) that are scarcely labelled and are often distributed across a network of the symbiont institutions. It is challenging to train the effective machine learning models on such data. In this work, we propose dynamic neural graphs based federated learning framework to address these challenges. The proposed framework extends Reptile, a model agnostic meta-learning (MAML) algorithm, to a federated setting. However, unlike the existing MAML algorithms, this paper proposes a dynamic variant of neural graph learning (NGL) to incorporate unlabelled examples in the supervised training setup. Dynamic NGL computes a meta-learning update by performing supervised learning on a labelled training example while performing metric learning on its labelled or unlabelled neighbourhood. This neighbourhood of a labelled example is established dynamically using local graphs built over the batches of training examples. Each local graph is constructed by comparing the similarity between embedding generated by the current state of the model. The introduction of metric learning on the neighbourhood makes this framework semi-supervised in nature. The experimental results on the publicly available MIMIC-III dataset highlight the effectiveness of the proposed framework for both single and multi-task settings under data decentralisation constraints and limited supervision.
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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.