Inferred edges come from two machine sources: sweeps of open repository
metadata (mostly SSRN and Zenodo), and a bulk OpenAlex citation snapshot. Every row below is
labelled with the one it came from. Neither is a complete citation index — absence here is not
evidence of absence.
Is this your work? Sign in with ORCID and put your name on the
claims that are yours.
Reference health. See whether every reference in this work resolves to a
known work and none has been retracted — and get a dated CiteStamped
badge you can embed.
Federated Learning and 5G/6G‐Based Internet of Medical Things (IoMT): Applications, Key Enabling Technologies, Open Issues and Future Research Directions
Blockchain- and Deep Learning-Empowered Resource Optimization in Future Cellular Networks, Edge Computing, and IoT: Open Challenges and Current Solutions
AI Integrated Blockchain Technology for Secure Health Care—Consent-Based Secured Federated Transfer Learning for Predicting COVID-19 on Wearable Devices
Advancing medical data classification through federated learning and blockchain incentive mechanism: implications for modern software systems and applications
A blockchain-based LLM-driven energy-efficient scheduling system towards distributed multi-agent manufacturing scenario of new energy vehicles within the circular economy
Federated learning review: Fundamentals, enabling technologies, and future applications
showing the first 200 of 868
Editorial review for independent work. ICSAC — the Institute for Complexity
Science and Advanced Computing — runs a structured editorial panel for researchers
publishing outside journals, and publishes on CiteStamp rails. Submission is free
and goes through ORCID: icsacinstitute.org/submit.