CoLLM: A Unified Framework for Co-execution of LLMs Federated Fine-tuning and Inference
arXiv 2604.16400•187b23301c1e15f29e24caddddbcef8e514e892838066f9d86545ededb98755b
B-PASTEDoS-riskHieraSparseKV-cacheLLMPEFTagentic-servingautonomous-systemscloud-probingco-executiondistributed-trainingedge-computingfederated-learninggraph-neural-networksgraph-transformerinference-optimizationlatency-estimationmicroservicespower-managementprivacyreconnaissanceside-channelsparse-attentionspeculative-executionspot-instances
Paper metadata
- arXiv ID
- 2604.16400
- Version
- Not specified by this published record
- Category
- Computer Science — Distributed, Parallel, and Cluster Computing (cs.DC)
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Evidence and limitations
- Source ID
- arxiv_cs_dc
- Record identifier
- 187b23301c1e15f29e24caddddbcef8e514e892838066f9d86545ededb98755b
- Enrichment time
- 2026-04-21T08:52:21Z
- AI-assisted enrichment
- Yes
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