OpenAlex
metadata only
CC BY 4.0
Federated Learning: A Survey on Enabling Technologies, Protocols, and Applications
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 kno
Conceptio Open Archive
Analyze & explore related research in Conceptio
Connect this record directly into your research workflow or AI agent. Explore citations across open repositories, query full text, and export structured proof bundles.
This document is indexed with metadata only: full text is not available in the archive for this record. Open the official source ↗
Related documents
- Scalability and Performance Evaluation of Federated Learning Frameworks: A Comparative Analysis#1109657
- Scalability and Performance Evaluation of Federated Learning Frameworks: A Comparative Analysis#919340
- FedSAP: Federated Learning with Structured Adaptive Partitioning for Multi-Domain Heterogeneous Edge Devices#1156909
- From Euclidean to Graph-Structured Data: A Survey of Collaborative Learning#925359
- From Euclidean to Graph-Structured Data: A Survey of Collaborative Learning#921123
Record · ID 976476
Retrieved via Conceptio: every document is proof-bundled with source, license, and retrieval metadata.
Retrieved via Conceptio: every document is proof-bundled with source, license, and retrieval metadata.
Bring this data into your agent: Connect via MCP or API · 20 free credits to start · no card required
Read the Docs → · View CLI source on GitHub · Conceptio Open Knowledge Archive
Read the Docs → · View CLI source on GitHub · Conceptio Open Knowledge Archive