arXiv (All)
open access
Bridging Compressed Communication and Local Steps via Spectral Method in Federated Learning
Federated Learning (FL) enables collaborative training of complex models across large-scale networks of edge devices while keeping data localized. However, data heterogeneity and high communication overhead remain fundamental bottlenecks. Recently, the spectral optimizer Muon has demonstrated remark
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
- Federated Learning in Mobile Edge Networks: A Comprehensive Survey#177303
- Federated Learning for IoT: A Survey of Techniques, Challenges, and Applications#341899
- Communication-efficient personalization in federated learning for edge devices#964153
- Reviving Stale Updates: Data-Free Knowledge Distillation for Asynchronous Federated Learning#1325050
Record · ID 1387708
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