MLFCIL: A Multi-Level Forgetting Mitigation Framework for Federated Class-Incremental Learning in LEO Satellites
arXiv 2604.02356•36f3c4b0169919d6b34da1e4dff83d0aac4c8cff02007c3a4aea5e3adf24244b
5g-slicing6g-securityagent-communicationai-sinkholecatastrophic-forgettingcensorshipddos-detectiondns-blockingdual-usefederated-learninggithub-disclosureleo-satellitesllm-classificationmulti-uav-deploymentnetwork-monitoringpi-holerl-controlroute-changesegmented-cardinalitysemantic-interoperabilitysuper-host-detectiontraceroute-detectionuav-relayvanet
Paper metadata
- arXiv ID
- 2604.02356
- Version
- Not specified by this published record
- Category
- Computer Science — Networking and Internet Architecture (cs.NI)
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Evidence and limitations
- Source ID
- arxiv_cs_ni
- Record identifier
- 36f3c4b0169919d6b34da1e4dff83d0aac4c8cff02007c3a4aea5e3adf24244b
- Enrichment time
- 2026-04-06T07:24:01Z
- AI-assisted enrichment
- Yes
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