MLFCIL: A Multi-Level Forgetting Mitigation Framework for Federated Class-Incremental Learning in LEO Satellites

2026-04-06T07:24:01Z36f3c4b0169919d6b34da1e4dff83d0aac4c8cff02007c3a4aea5e3adf24244b
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

What happened

This feed aggregates recent arXiv papers spanning networking, wireless systems, and AI agent architectures with several security-relevant contributions and dual‑use concerns. Notable items: AI-Sinkhole — an AI-agent augmented DNS discovery/classification + Pi‑Hole blocking framework (with published code) enabling temporary, network-wide blocking of emerging LLM/chatbot services (explainable classifiers using quantized LLMs); TRACE — traceroute-latency based route-change detection (endpoint-only monitoring for routing instability); SegSketch — subnet-aware segmented cardinality estimation to 개선

Why it matters

A reviewed impact interpretation has not been published for this record.

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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