arXiv (All)
open access
Wireless Foundation Models: State-of-the-Art and Open Challenges
Wireless foundation models (WFMs) have emerged as a promising approach for learning reusable representations from large-scale wireless data and adapting them to downstream tasks. However, the rapidly growing literature remains fragmented across modalities, pretraining objectives, architectures, adap
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
- Hierarchical Federated Learning for Unsupervised Waveform Classification over Tactical MANETs#998256
- From Task Mixtures to Specialized Experts#1155515
- FedSAP: Federated Learning with Structured Adaptive Partitioning for Multi-Domain Heterogeneous Edge Devices#1156909
- Structuring Relations Among Learning Paradigms via Protocol--Objective--Resource Reductions#1117097
- Scheduling algorithms for the optimization of distributed machine learning models on heterogeneous resources#1146095
Record · ID 927974
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