arXiv (All)
open access
FedSubMuon: Communication-Efficient Federated LLM Fine-Tuning via Structured Subspace Muon
Federated fine-tuning adapts large language models (LLMs) to decentralized client data, but its scalability in cross-device training is often limited by the high communication cost. Muon is an optimizer that improves optimization performance by orthogonalizing momentum for matrix-valued parameters.
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
- Just Talk Once: Communication-Efficient Split Federated LLM Fine-Tuning on Edge Devices#821496
- FlashCP: Load-Balanced Communication-Efficient Context Parallelism for LLM Training#267627
- Compass: Dissecting Communication and Computation Operators for Efficient LLM Training#997555
- Distributed Generative Inference of LLM at Internet Scales with Multi-Dimensional Communication Optimization#126492
- RW-LoRA: Communication-Efficient Decentralized LoRA Fine-Tuning via Random Walks#819555
Record · ID 973681
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