Omar, “Blockchain for deep learning: review and open challenges,” Cluster Computing, pp

The gap

Evidence profile

Sourced from the future work and inline gaps of the source papers, classified as general, drawn from work published between 2023 and 2026, spanning 3 journals. Those papers have been cited 183 times in total.

Supporting evidence — 3 representative gaps

• An Optimized Machine Learning Framework for Smart Grid Energy Management (2026) · International Journal of Advanced Research in Science Communication and Technology · doi

  • Explainable and Trustworthy AI: Future smart grids should also have models that are explainable with high prediction accuracy, improving the transparency of the decision-making process, increasing the confidence of the smart grid operators and assisting the smart grid operators to fulfill regulation requirements. • Edge Computing and Federated Learning: Utilizing federated learning and edge intelligence can alleviate communication latency, resolve consumer privacy concerns, and enable a decentralized smart grid energy management system. • Digital Twin-Based Smart Grids: Digital twin simulates, predict, diagnose, and optimize digital twin operations, improving the resilience of digital twin operations, and cutting maintenance costs. • Blockchain-Enabled Energy Trading: Blockchain technology has the potential to create new local energy markets, provide transaction security and integrity, and enable the decentralized, safe, and transparent movement of energy between people.

  generalfuture work
  Keywords: smart energy digital twin grid explainable grids improving operators edge federated learning enable decentralized operations
• A Secure Decentralized Blockchain and Machine Learning-Based Peer-to-Peer Energy Trading in a Smart Grid (2026) · Sustainability · doi

  Future work should focus on improving the practicality and scalability of the proposed P2P energy trading framework. Real-time integration with IoT devices and smart meters https://doi.org/10.3390/su18178694 Sustainability 2026, 18, 8694 47 of 54 can replace simulated inputs and enable deployment in real-world environments. The blockchain platform can be extended from a private network to a public or consortium blockchain to improve scalability and transparency. Machine learning can also be fully integrated with smart contracts through oracle-based communication, while advanced techniques such as deep learning or reinforcement learning may further enhance load forecasting and dynamic pricing. In addition, future studies should develop a user-friendly mobile application for monitoring energy usage and token transactions, incorporate regula- tory and grid compliance requirements, strengthen data security through cryptographic protection of smart meter data, and upgrade smart contracts to newer Solidity versions with formal security verification. Further validation should include large-scale stress testing with over 100 users, deployment on public blockchain networks, more comprehensive False Data Injection Attack (FDIA) evaluations, grid-aware auction mechanisms that con- sider physical network constraints, improved token economic models, real-world pilot deployments with microgrid operators, and controlled experiments comparing system performance with and without blockchain and machine learning integration. Author Contributions: Conceptualization, S.F. and M.J.A.; methodology, S.F.; software, S.F.; val- idation, S.F. and M.J.A.; formal analysis, S.F.; investigation, S.F.; resources, S.F. and M.J.A.; data curation, S.F.; writing—original draft preparation, S.F.; writing—review and editing, S.F. and M.J.A.; visualization, S.F.; supervision, M.J.A.; project administration, M.J.A.; funding acquisition, S.F. and M.J.A. All authors have read and agreed to the published version of the manuscript. Funding: This research received no external funding. Institutional Review Board Statement: Not applicable. Informed Consent Statement: Not applicable. Data Availability Statement: The original contributions presented in this study are included in the article. Further inquiries can be directed to the corresponding author. Conflicts of Interest: The authors declare no conflict of interest.

  generalfuture work
  Keywords: smart blockchain learning real further funding statement future scalability energy integration deployment world network public
• A Survey of Blockchain and Artificial Intelligence for 6G Wireless Communications (2023) · IEEE Communications Surveys & Tutorials · cited 183× · doi

  Omar, “Blockchain for deep learning: review and open challenges,” Cluster Computing, pp. , “Transformative effects of iot, blockchain and artificial intelligence on cloud computing: Evolution, vision, trends and open challenges,” Internet of Things, vol. J¨antti, “Reinforcement learning in blockchain-enabled IIoT net- works: A survey of recent advances and open challenges,” Sustain- ability, vol.

  generalinline gaps
  Keywords: blockchain open challenges learning computing omar deep review cluster transformative effects artificial intelligence cloud evolution

Questions about this gap