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Review regressions before expanding the test." ], "measure": "Successful attacks missed, legitimate actions blocked, detection delay, and review cost.", "decision": "Keep the change only if it meets your preselected quality and latency limits and improves the metric you care about after setup and operating costs. Otherwise retain the baseline. This is an exploration plan, not a validated recommendation." }, "builder_review": null, "arxiv_id": "2609.40312", "title": "Compression Footprints as Security Signals for Model-Poisoning Defense in Federated Learning", "summary": "CRAFT replaces geometry-based robust aggregation rules (e.g., Krum, trimmed mean) in compressed federated learning with a server-side trust score derived from lossy-compression distortion and payload statistics, requiring no client metadata, no extra communication, and no knowledge of the malicious-client count. 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