{"paper":{"title":"Global Unknown Estimation: A Statistical Framework for Wireless Distributed Learning","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":[],"primary_cat":"eess.SP","authors_text":"Ali Bereyhi, Ben Liang, Yicheng Qu","submitted_at":"2026-06-01T21:04:50Z","abstract_excerpt":"Over-the-air computation (AirComp) is widely used for model aggregation in wireless distributed learning. Although it enhances communication efficiency, we believe the AirComp aggregation has limited effectiveness due to the difference between its target problem and that of distributed learning. In this paper, we develop a rigorous formulation for optimal model aggregation in wireless distributed learning. Using this formulation, we show that AirComp aggregation generally assumes a mismatched statistical model for local parameters. We then propose a statistical framework for model aggregation,"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2606.02891","kind":"arxiv","version":1},"verdict":{"id":null,"model_set":{},"created_at":null,"strongest_claim":"","one_line_summary":"","pipeline_version":null,"weakest_assumption":"","pith_extraction_headline":""},"integrity":{"clean":true,"summary":{"advisory":0,"critical":0,"by_detector":{},"informational":0},"endpoint":"/pith/2606.02891/integrity.json","findings":[],"available":true,"detectors_run":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938"},"references":{"count":0,"sample":[],"resolved_work":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57","internal_anchors":0},"formal_canon":{"evidence_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"author_claims":{"count":0,"strong_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"builder_version":"pith-number-builder-2026-05-17-v1"}