PowerSlider: Exploiting Phase Asymmetry for LLM Serving under Demand Response
arXiv 2608.21719•31716ee2cda9342d2afd10538f23e856c5818f2ed0b0555181081cf75e575f6b
artificial-intelligenceblockchainbyzantine-resilienceconcurrent-systemsconsensus-protocolsedge-aifederated-learningformal-verificationgpu-computinginformation-leakagelightning-networkllm-servingmodel-poisoningpayment-channelssecurity-research
Paper metadata
- arXiv ID
- 2608.21719
- 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
- 31716ee2cda9342d2afd10538f23e856c5818f2ed0b0555181081cf75e575f6b
- Enrichment time
- 2026-08-25T08:52:12Z
- AI-assisted enrichment
- Yes
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