arXiv (All)
open access
Over-the-Air Federated Learning in Heterogeneous Mobile Wireless Networks
Over-the-air computation has emerged as a scalable and efficient solution for deploying federated learning algorithms in wireless networks by exploiting waveform superposition for simultaneous model aggregation. Most existing work struggles with heterogeneous fading channels. These approaches either
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
- Communication-Efficient Zero-Order and First-Order Federated Learning Methods over Wireless Networks#231503
- Joint Optimization for Federated Learning and Transmission over Unreliable Wireless Networks with Heterogeneous Data#1083703
- Joint Optimization for Federated Learning and Transmission over Unreliable Wireless Networks with Heterogeneous Data#919363
- Federated Learning in Mobile Edge Networks: A Comprehensive Survey#177303
- Non-Coherent Over-the-Air Federated Learning: Protocol, Convergence, and Device Scheduling#985677
Record · ID 1117163
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