{"schema":"https://assignee.net/schemas/correction-record-v1","schema_version":"1.0","contract_version":"correction-record-v1.0","contract_updated":"2026-06-01","generated_from":"public correction metadata","publisher":{"name":"Assignee Research","url":"https://assignee.net"},"work":{"task_id":"e1ea6a8c-28d3-43ec-a424-d9fa1ef21104","short_id":"e1ea6a8c","title":"Supervised and Unsupervised Federated Learning for Zero-Day Malware Detection in IoT Deployments","url":"https://assignee.net/paper/e1ea6a8c-28d3-43ec-a424-d9fa1ef21104","published_date":"2026-05-30","doi":null},"manifest":{"schema":"https://assignee.net/schemas/paper-manifest-v1","contract_version":"paper-manifest-v1.1","contract_updated":"2026-06-01","current_manifest_url":"https://assignee.net/paper/e1ea6a8c-28d3-43ec-a424-d9fa1ef21104/manifest.json","correction_record_url":"https://assignee.net/paper/e1ea6a8c-28d3-43ec-a424-d9fa1ef21104/corrections.json"},"correction_state":"CURRENT","correction_count":0,"last_corrected_at":null,"correction_events":[],"known_superseded_manifest_urls":[],"policy":{"corrections_are_additive":true,"no_silent_public_rewrites":true,"private_operational_logs_public":false,"basis":"Public corrections should be represented as additive records instead of silent replacement of public interpretation."},"interpretation":"CURRENT means no public correction event is attached to this work. It is not a claim that the generated synthesis is error-free.","limitations":["The correction record describes public correction metadata only.","No public correction event is attached unless it appears in correction_events.","Private review notes, local paths, and operational logs are not exposed."]}