arXiv (All)
open access
Training Machine Learning models at the Edge: A Survey
Edge computing has gained significant traction in recent years, promising enhanced efficiency by integrating artificial intelligence capabilities at the edge. While the focus has primarily been on the deployment and inference of Machine Learning (ML) models at the edge, the training aspect remains l
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
- Adaptive DNN Partitioning and Offloading in Heterogeneous Edge-Cloud Continuum#175171
- Towards smart and adaptive agents for active sensing on edge devices#1011990
- EdgeCraft: Automated Model Crafting for Edge IoT#1129764
- EdgeCraft: Automated Model Crafting for Edge IoT#1108700
- Smart Adaptive Computing Across the Continuum: LLMs in IoT-Edge-Cloud Resource Management#987105
Record · ID 1389035
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