{"schema_version":"papers/paper-detail-v1","title":"FedZMG: Efficient Client-Side Optimization in Federated Learning","surface":"papers","opportunity_kernel":{"paper_id":"ddb814e1-a221-4141-884e-126f67d230cc","title":"FedZMG: Efficient Client-Side Optimization in Federated Learning","authors":[],"arxiv_id":"2602.18384v1","doi":null,"published_at":"2026-02-20T17:45:28.000Z","score_object":{"overall":{"value":7,"scale":"0-10","confidence":0.85,"confidence_reason":"Backfilled from persisted papers.viability_score.","model_version":"phase0-backfill-v1","pipeline_version":"phase0-kernel-v1","computed_at":"2026-04-02T02:30:40.136Z","fresh_until":"2026-05-02T02:30:40.136Z","is_stale":true,"source_count":1,"missingness":[]},"technical":{"value":0,"scale":"0-10","confidence":0.15,"confidence_reason":"No persisted technical score source was available; marked low confidence.","model_version":"phase0-backfill-v1","pipeline_version":"phase0-kernel-v1","computed_at":"2026-04-02T02:30:40.136Z","fresh_until":"2026-04-16T02:30:40.136Z","is_stale":true,"source_count":0,"missingness":["reproducibility_results.reproducibility_score","deployability_scores.score","paper_extraction_scorecards.reconstruction_score"]},"commercial":{"value":0,"scale":"0-10","confidence":0.75,"confidence_reason":"Backfilled from persisted commercial_flags and repo availability.","model_version":"phase0-backfill-v1","pipeline_version":"phase0-kernel-v1","computed_at":"2026-04-02T02:30:40.136Z","fresh_until":"2026-05-02T02:30:40.136Z","is_stale":true,"source_count":1,"missingness":[]},"market":{"value":0,"scale":"0-10","confidence":0.15,"confidence_reason":"No persisted distribution_readiness_scores row was available; marked low confidence.","model_version":"phase0-backfill-v1","pipeline_version":"phase0-kernel-v1","computed_at":"2026-04-02T02:30:40.136Z","fresh_until":"2026-04-16T02:30:40.136Z","is_stale":true,"source_count":0,"missingness":["distribution_readiness_scores.score"]},"team":{"value":10,"scale":"0-10","confidence":0.15,"confidence_reason":"No persisted team-quality evidence was available; marked low confidence.","model_version":"phase0-backfill-v1","pipeline_version":"phase0-kernel-v1","computed_at":"2026-04-02T02:30:40.136Z","fresh_until":"2026-04-16T02:30:40.136Z","is_stale":true,"source_count":0,"missingness":["engineer_profiles.builder_score","author_startups"]},"methodology":{"value":0,"scale":"0-10","confidence":0.15,"confidence_reason":"No persisted methodology score source was available; marked low confidence.","model_version":"phase0-backfill-v1","pipeline_version":"phase0-kernel-v1","computed_at":"2026-04-02T02:30:40.136Z","fresh_until":"2026-05-02T02:30:40.136Z","is_stale":true,"source_count":0,"missingness":["paper_extraction_scorecards.total_score","paper_extraction_scorecards.standard_extraction_score"]}},"evidence_receipt":{"freshness":"stale","proof_status":"unverified","repo_status":"missing","references_count":0,"source_count":0,"coverage":0.1667,"missingness":["repo_url","references","proof_status","distribution_readiness_scores","paper_extraction_scorecards"],"unresolved_unknowns":["distribution readiness has not been computed yet","proof verification has not been recorded yet"],"last_verification_at":"2026-04-02T02:30:40.136Z"},"lineage_hash":"ded0bd9f20fc4042015381cb69712a247db62418b160a5752d7363d892f03080"},"distribution":null,"replication_evidence":[],"author_dna":[]}