ArticleIEEE access : practical innovations, open solutions2020
Federated Learning: A Survey on Enabling Technologies, Protocols, and Applications.
Article in IEEE access : practical innovations, open solutions, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 40 papers, 1 of them a synthesis that pooled it.
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
40 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Towards Integrated Air Pollution Monitoring and Health Impact Assessment Using Federated Learning: A Systematic Review.Frontiers in public health · 2022Pooled it
- Privacy-aware continuous federated biometric authentication (PACFBA) using federated deep learning: a novel usable security framework.Scientific reports · 2026Article
- Federated learning frameworks: quality and interoperability for biomedical research.NAR genomics and bioinformatics · 2026Review
- Integrating NLP and Ensemble Learning into Next-Generation Firewalls for Robust Malware Detection in Edge Computing.Sensors (Basel, Switzerland) · 2026Article
- AI-Driven Personalized Nutrition: Integrating Omics, Ethics, and Digital Health.Molecular nutrition & food research · 2025Review
- Lightweight Federated Learning Approach for Resource-Constrained Internet of Things.Sensors (Basel, Switzerland) · 2025Article
- A privacy-preserving dependable deep federated learning model for identifying new infections from genome sequences.Scientific reports · 2025Article
- Revolutionizing healthcare data analytics with federated learning: A comprehensive survey of applications, systems, and future directions.Computational and structural biotechnology journal · 2025Review
- Achieving consistency in FedSAM using local adaptive distillation on sports image classification.PloS one · 2025Article
- FedNolowe: A normalized loss-based weighted aggregation strategy for robust federated learning in heterogeneous environments.PloS one · 2025Article
- Federated learning framework for IoT intrusion detection using tab transformer and nature-inspired hyperparameter optimization.Frontiers in big data · 2025Article
- An analysis of decipherable red blood cell abnormality detection under federated environment leveraging XAI incorporated deep learning.Scientific reports · 2024Article
- Federated Learning-Oriented Edge Computing Framework for the IIoT.Sensors (Basel, Switzerland) · 2024Article
- Orbital learning: a novel, actively orchestrated decentralised learning for healthcare.Scientific reports · 2024Article
- Decentralized Mixed Effects Modeling in COINSTAC.Neuroinformatics · 2024Article
- Smart home system using blockchain technology in green lighting environment in rural areas.Heliyon · 2024Article
- CODA: an open-source platform for federated analysis and machine learning on distributed healthcare data.Journal of the American Medical Informatics Association : JAMIA · 2024Article
- A Survey on Heterogeneity Taxonomy, Security and Privacy Preservation in the Integration of IoT, Wireless Sensor Networks and Federated Learning.Sensors (Basel, Switzerland) · 2024Article
- Enhancing sports image data classification in federated learning through genetic algorithm-based optimization of base architecture.PloS one · 2024Article
- Privacy preserved and decentralized thermal comfort prediction model for smart buildings using federated learning.PeerJ. Computer science · 2024Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
4 authors.
Funding
Abstract
This paper provides a comprehensive study of Federated Learning (FL) with an emphasis on enabling software and hardware platforms, protocols, real-life applications and use-cases. FL can be applicable to multiple domains but applying it to different industries has its own set of obstacles. FL is known as collaborative learning, where algorithm(s) get trained across multiple devices or servers with decentralized data samples without having to exchange the actual data. This approach is radically different from other more established techniques such as getting the data samples uploaded to servers or having data in some form of distributed infrastructure. FL on the other hand generates more robust models without sharing data, leading to privacy-preserved solutions with higher security and access privileges to data. This paper starts by providing an overview of FL. Then, it gives an overview of technical details that pertain to FL enabling technologies, protocols, and applications. Compared to other survey papers in the field, our objective is to provide a more thorough summary of the most relevant protocols, platforms, and real-life use-cases of FL to enable data scientists to build better privacy-preserving solutions for industries in critical need of FL. We also provide an overview of key challenges presented in the recent literature and provide a summary of related research work. Moreover, we explore both the challenges and advantages of FL and present detailed service use-cases to illustrate how different architectures and protocols that use FL can fit together to deliver desired results.
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What OpenQuestion holds
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.