ArticleCluster computing2023
Blockchain for deep learning: review and open challenges.
Article in Cluster computing, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
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
15 citing papers in PubMed.
- Authentication Mechanisms in the Internet of Things: A Comparative Analysis Across RFID, Smart Grids, Vehicular Networks, and Smart Homes.Sensors (Basel, Switzerland) · 2026Article
- Fastformer: An Efficient Attention-Based Framework for Rapid Multi-Class Fault Diagnosis in High-End Equipment Vibration Signals.Entropy (Basel, Switzerland) · 2026Article
- Federated deep blockchain-based system for secure verification of academic transcripts and matching study plans in Saudi universities.Scientific reports · 2026Article
- Estimating Fibrosity Scores of Plant-Based Meat Products from Images: A Deep Neural Network Approach.Foods (Basel, Switzerland) · 2026Article
- Ascertaining Susceptibilities in Smart Contracts: A Quantum Machine Learning Approach.Entropy (Basel, Switzerland) · 2025Article
- Hierarchical Swin Transformer Ensemble with Explainable AI for Robust and Decentralized Breast Cancer Diagnosis.Bioengineering (Basel, Switzerland) · 2025Article
- A blockchain based deep learning framework for a smart learning environment.Scientific reports · 2025Article
- Integrating blockchain technology with artificial intelligence for the diagnosis of tibial plateau fractures.European journal of trauma and emergency surgery : official publication of the European Trauma Society · 2025Article
- A Method for the Spatial Interpolation of EEG Signals Based on the Bidirectional Long Short-Term Memory Network.Sensors (Basel, Switzerland) · 2024Article
- Blockchain security enhancement: an approach towards hybrid consensus algorithms and machine learning techniques.Scientific reports · 2024Article
- Assessment System for Child Head Injury from Falls Based on Neural Network Learning.Sensors (Basel, Switzerland) · 2023Article
- A Blockchain-Based Trustworthy Model Evaluation Framework for Deep Learning and Its Application in Moving Object Segmentation.Sensors (Basel, Switzerland) · 2023Article
- Investigating the Regulatory Process, Safety, Efficacy and Product Transparency for Nutraceuticals in the USA, Europe and Australia.Foods (Basel, Switzerland) · 2023Review
- Next-generation predictive maintenance: leveraging blockchain and dynamic deep learning in a domain-independent system.PeerJ. Computer science · 2023Article
- A rapid review of machine learning approaches for telemedicine in the scope of COVID-19.Artificial intelligence in medicine · 2022Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
6 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Deep learning has gained huge traction in recent years because of its potential to make informed decisions. A large portion of today's deep learning systems are based on centralized servers and fall short in providing operational transparency, traceability, reliability, security, and trusted data provenance features. Also, training deep learning models by utilizing centralized data is vulnerable to the single point of failure problem. In this paper, we explore the importance of integrating blockchain technology with deep learning. We review the existing literature focused on the integration of blockchain with deep learning. We classify and categorize the literature by devising a thematic taxonomy based on seven parameters; namely, blockchain type, deep learning models, deep learning specific consensus protocols, application area, services, data types, and deployment goals. We provide insightful discussions on the state-of-the-art blockchain-based deep learning frameworks by highlighting their strengths and weaknesses. Furthermore, we compare the existing blockchain-based deep learning frameworks based on four parameters such as blockchain type, consensus protocol, deep learning method, and dataset. Finally, we present important research challenges which need to be addressed to develop highly trustworthy deep learning frameworks.
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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.