arXiv (All)
open access
Compression Footprints as Security Signals for Model-Poisoning Defense in Federated Learning
Lossy compression is widely used in Federated Learning (FL) but is generally treated as an error source, while conventional poisoning defenses inspect update geometry. In this work, we instead treat the compressor's response as a security signal: the input-dependent distortion and payload behavior i
Conceptio Open Archive
Analyze & explore related research in Conceptio
Connect this record directly into your research workflow or AI agent. Explore citations across open repositories, query full text, and export structured proof bundles.
This document is indexed with metadata only: full text is not available in the archive for this record. Open the official source ↗
Related documents
- Compression Footprints as Security Signals for Model-Poisoning Defense in Federated Learning#1136811
- SteganoBackdoor: Evading Data-Poisoning Defenses via Steganographic Backdoors#1034684
- Different Corruptions, Different Signals: Uncertainty and Loss in Federated Data Quality#1106153
- Revisiting Code Debloating with Ground Truth-based Evaluation#120588
- Randomness Improvements for Security Protocols#399437
Record · ID 1154572
Retrieved via Conceptio: every document is proof-bundled with source, license, and retrieval metadata.
Retrieved via Conceptio: every document is proof-bundled with source, license, and retrieval metadata.
Bring this data into your agent: Connect via MCP or API · 20 free credits to start · no card required
Read the Docs → · View CLI source on GitHub · Conceptio Open Knowledge Archive
Read the Docs → · View CLI source on GitHub · Conceptio Open Knowledge Archive