Performance and Energy Trade-Off Analysis of Hierarchical Federated Learning for Plant Disease Classification
arXiv 2605.08121•504237eb34e44d69b8089144374b36dfc64d37f20fa8ece8236961661c7d5a5d
BFTIDSLEO-satellitesLLM-servingRL-as-a-servicebyzantinecloud-orchestrationconsensuscryptographic-verificationdistributed-systemsfederated-learningformal-methodsintrusion-detectionmodel-poisoningmulti-tenancyparallel-computationprivacyspeculative-decodingstability-analysiszero-knowledge-proofs
Paper metadata
- arXiv ID
- 2605.08121
- 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
- 504237eb34e44d69b8089144374b36dfc64d37f20fa8ece8236961661c7d5a5d
- Enrichment time
- 2026-05-12T08:52:19Z
- AI-assisted enrichment
- Yes
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