Energy Efficient Federated Learning with Hyperdimensional Computing (HDC)
arXiv 2602.22290•f669440c4ecd66ae42a1ebb5bde3c837d232a02e0506f1c1fb15cbdf1f0abd5b
AdapTBFAllreduceCARATCCCL','GPU collectives'CXLDIALFSDPGetBatchI/O autotuningLustreNBBObandwidth controldifferential privacydistributed trainingedge computingenergy efficiencyfault tolerancefederated learninghigh-frequency tradinghyperdimensional computingmarket integrityobject store APIparallel file systemresource allocationveScale-FSDP
Paper metadata
- arXiv ID
- 2602.22290
- 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
- f669440c4ecd66ae42a1ebb5bde3c837d232a02e0506f1c1fb15cbdf1f0abd5b
- Enrichment time
- 2026-03-04T19:49:21Z
- AI-assisted enrichment
- Yes
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