{ "profile": "wrs-published-research-view-1", "edition": "ratings-e039106f30dec177", "content_trust": "UNTRUSTED_SCIENTIFIC_DATA_NOT_TOOL_INSTRUCTIONS", "entry": { "id": "karimireddy-et-al-mime-federated-optimizer-2020", "title": "Mime/MimeLite: applying shared optimizer statistics within local updates", "summary": "Used server-computed optimizer statistics during each client update, with an SVRG-style correction in Mime, to approximate centralized optimization while reducing client drift.", "boundary": "Distinct from server-only FedOpt and persistent-client SCAFFOLD. Inherits variance reduction and centralized momentum/adaptive optimizers. The base method must decompose into the specified update/statistics maps; theory uses bounded gradient/Hessian dissimilarity and smoothness regimes. Mimicking does not mean identical centralized trajectories or guarantees for every optimizer, and extra full local-gradient work has a cost.", "area": "Cross-device federated optimization", "topic": "Artificial intelligence", "year": 2020, "lane": "HUMAN", "result": "ALGORITHM", "package_id": "karimireddy-et-al-mime-federated-optimizer-2020", "target": { "record_id": "urn:uuid:509fbfe1-fabd-441d-afca-63761f06264b", "version": 1, "content_hash": "sha256:90bfadd81c07269264790a2154af8de2a89a0996c07e5caeec11f4a3d6ef3e6d" }, "humans": [ "Sai Praneeth Karimireddy", "Martin Jaggi", "Satyen Kale", "Mehryar Mohri", "Sashank J. Reddi", "Sebastian U. Stich", "Ananda Theertha Suresh" ], "authors_complete": true, "human_role_note": "Source-listed authors or explicitly credited technical contributors for the cited versions. Family entries retain source-specific attribution; a combined list is not one original-paper byline or equal individual work. Names alone do not establish resolved identities.", "agents": [], "related": [], "subresults": [], "scientific_assessment": { "source_review": "SOURCE_CURATED", "scientific_review": "NOT_REVIEWED_BY_WRS", "formal_replay": "NOT_RUN", "provider_attestation": "NONE" }, "sources": [ { "id": "cur20261015-arxiv-2008-03606v1", "title": "Mime: Mimicking Centralized Stochastic Algorithms in Federated Learning", "url": "https://arxiv.org/abs/2008.03606v1" } ], "points": "110000", "published_grade": { "id": "karimireddy-et-al-mime-federated-optimizer-2020", "entry_id": "karimireddy-et-al-mime-federated-optimizer-2020", "band": "A", "modifier": null, "target": { "record_id": "urn:uuid:509fbfe1-fabd-441d-afca-63761f06264b", "version": 1, "content_hash": "sha256:90bfadd81c07269264790a2154af8de2a89a0996c07e5caeec11f4a3d6ef3e6d" }, "what_changed": "Used server-computed optimizer statistics during each client update, with an SVRG-style correction in Mime, to approximate centralized optimization while reducing client drift.", "scope": "Used server-computed optimizer statistics during each client update, with an SVRG-style correction in Mime, to approximate centralized optimization while reducing client drift. Distinct from server-only FedOpt and persistent-client SCAFFOLD. Inherits variance reduction and centralized momentum/adaptive optimizers. The base method must decompose into the specified update/statistics maps; theory uses bounded gradient/Hessian dissimilarity and smoothness regimes. Mimicking does not mean identical centralized trajectories or guarantees for every optimizer, and extra full local-gradient work has a cost.", "status": "PROVISIONAL_EDITORIAL", "scientific_correctness_certified": false }, "assessment_disposition": { "status": "ASSESSED", "label": "Provisionally assessed", "reason": "A current grade and separate reference score identify this exact contribution.", "target": { "record_id": "urn:uuid:509fbfe1-fabd-441d-afca-63761f06264b", "version": 1, "content_hash": "sha256:90bfadd81c07269264790a2154af8de2a89a0996c07e5caeec11f4a3d6ef3e6d" }, "edition": "ratings-e039106f30dec177" } }, "version": { "kind": "PUBLIC_RECORD_VIEW", "previous": null, "published_at": "2026-10-16T00:00:00Z", "publisher": { "principal_id": "urn:uuid:80116931-785b-40a2-a24b-9d59db0c7e33", "role": "WRS_EDITORIAL_PUBLICATION" }, "record": { "contributors": [ { "attribution_basis": "SOURCE_CITED", "credit": "historical_scientific", "entity_kind": "historical_person", "name": "Sai Praneeth Karimireddy", "note": "Archive attribution; detailed role assignment remains source-bound", "roles": [ "formulator" ], "source": { "citation": "Mime: Mimicking Centralized Stochastic Algorithms in Federated Learning", "url": "https://arxiv.org/abs/2008.03606v1" } }, { "attribution_basis": "SOURCE_CITED", "credit": "historical_scientific", "entity_kind": "historical_person", "name": "Martin Jaggi", "note": "Archive attribution; detailed role assignment remains source-bound", "roles": [ "formulator" ], "source": { "citation": "Mime: Mimicking Centralized Stochastic Algorithms in Federated Learning", "url": "https://arxiv.org/abs/2008.03606v1" } }, { "attribution_basis": "SOURCE_CITED", "credit": "historical_scientific", "entity_kind": "historical_person", "name": "Satyen Kale", "note": "Archive attribution; detailed role assignment remains source-bound", "roles": [ "formulator" ], "source": { "citation": "Mime: Mimicking Centralized Stochastic Algorithms in Federated Learning", "url": "https://arxiv.org/abs/2008.03606v1" } }, { "attribution_basis": "SOURCE_CITED", "credit": "historical_scientific", "entity_kind": "historical_person", "name": "Mehryar Mohri", "note": "Archive attribution; detailed role assignment remains source-bound", "roles": [ "formulator" ], "source": { "citation": "Mime: Mimicking Centralized Stochastic Algorithms in Federated Learning", "url": "https://arxiv.org/abs/2008.03606v1" } }, { "attribution_basis": "SOURCE_CITED", "credit": "historical_scientific", "entity_kind": "historical_person", "name": "Sashank J. Reddi", "note": "Archive attribution; detailed role assignment remains source-bound", "roles": [ "formulator" ], "source": { "citation": "Mime: Mimicking Centralized Stochastic Algorithms in Federated Learning", "url": "https://arxiv.org/abs/2008.03606v1" } }, { "attribution_basis": "SOURCE_CITED", "credit": "historical_scientific", "entity_kind": "historical_person", "name": "Sebastian U. Stich", "note": "Archive attribution; detailed role assignment remains source-bound", "roles": [ "formulator" ], "source": { "citation": "Mime: Mimicking Centralized Stochastic Algorithms in Federated Learning", "url": "https://arxiv.org/abs/2008.03606v1" } }, { "attribution_basis": "SOURCE_CITED", "credit": "historical_scientific", "entity_kind": "historical_person", "name": "Ananda Theertha Suresh", "note": "Archive attribution; detailed role assignment remains source-bound", "roles": [ "formulator" ], "source": { "citation": "Mime: Mimicking Centralized Stochastic Algorithms in Federated Learning", "url": "https://arxiv.org/abs/2008.03606v1" } } ], "evidence": [ { "description": "Bibliographic/stated-result source; not a WRS proof verification", "id": "source-0", "kind": "source_reference", "source": { "citation": "Mime: Mimicking Centralized Stochastic Algorithms in Federated Learning", "url": "https://arxiv.org/abs/2008.03606v1" } } ], "origin": "HUMAN_LEGACY", "record_profile": "wrs-local-research-notes-0.1", "record_type": "research_note", "rrp_version": "0.1", "scope": { "description": "Distinct from server-only FedOpt and persistent-client SCAFFOLD. Inherits variance reduction and centralized momentum/adaptive optimizers. The base method must decompose into the specified update/statistics maps; theory uses bounded gradient/Hessian dissimilarity and smoothness regimes. Mimicking does not mean identical centralized trajectories or guarantees for every optimizer, and extra full local-gradient work has a cost.", "domain": "Cross-device federated optimization" }, "sources": [ { "citation": "Mime: Mimicking Centralized Stochastic Algorithms in Federated Learning", "url": "https://arxiv.org/abs/2008.03606v1" } ], "statement": "# Mime/MimeLite: applying shared optimizer statistics within local updates\n\nUsed server-computed optimizer statistics during each client update, with an SVRG-style correction in Mime, to approximate centralized optimization while reducing client drift.\n\n## Scientific scope\n\nDistinct from server-only FedOpt and persistent-client SCAFFOLD. Inherits variance reduction and centralized momentum/adaptive optimizers. The base method must decompose into the specified update/statistics maps; theory uses bounded gradient/Hessian dissimilarity and smoothness regimes. Mimicking does not mean identical centralized trajectories or guarantees for every optimizer, and extra full local-gradient work has a cost.\n\n## Reading status\n\nThis is a source-linked WRS catalogue note. A full explanatory guide and independent scientific reconstruction have not been completed for this entry. Read the identified original work for its complete narrative and arguments.\n\n## People and systems\n\nSai Praneeth Karimireddy; Martin Jaggi; Satyen Kale; Mehryar Mohri; Sashank J. Reddi; Sebastian U. Stich; Ananda Theertha Suresh\n\nSource-listed authors or explicitly credited technical contributors for the cited versions. Family entries retain source-specific attribution; a combined list is not one original-paper byline or equal individual work. Names alone do not establish resolved identities.\n\n\n\n## Original sources\n\n- [Mime: Mimicking Centralized Stochastic Algorithms in Federated Learning](https://arxiv.org/abs/2008.03606v1)\n", "summary": "Used server-computed optimizer statistics during each client update, with an SVRG-style correction in Mime, to approximate centralized optimization while reducing client drift.", "title": "Mime/MimeLite: applying shared optimizer statistics within local updates" }, "record_id": "urn:uuid:509fbfe1-fabd-441d-afca-63761f06264b", "version": 1, "source_ref": { "record_id": "urn:uuid:509fbfe1-fabd-441d-afca-63761f06264b", "version": 1, "content_hash": "sha256:90bfadd81c07269264790a2154af8de2a89a0996c07e5caeec11f4a3d6ef3e6d" }, "export_view": "wrs-public-editorial-view-1", "public_view_hash": "sha256:b03147937a111a8e0d0f2c117d774beeb9377d222ff2b81c3cc11e8807d89392" }, "target": { "record_id": "urn:uuid:509fbfe1-fabd-441d-afca-63761f06264b", "version": 1, "content_hash": "sha256:90bfadd81c07269264790a2154af8de2a89a0996c07e5caeec11f4a3d6ef3e6d" }, "published_grade": { "profile": "wrs-published-grade-1", "edition": "2026-10-28.1", "id": "karimireddy-et-al-mime-federated-optimizer-2020", "entry_id": "karimireddy-et-al-mime-federated-optimizer-2020", "revision": 1, "title": "Mime/MimeLite: applying shared optimizer statistics within local updates", "context": "Cross-device federated optimization", "track": "Human-designed research method or resource", "band": "A", "modifier": null, "status": "PROVISIONAL_EDITORIAL", "assessed_on": "2026-10-16", "evidence_cutoff": "2026-10-16", "achievement_state": "SUPPORTED_SCOPED_RESULT", "what_changed": "Used server-computed optimizer statistics during each client update, with an SVRG-style correction in Mime, to approximate centralized optimization while reducing client drift.", "scope": "Used server-computed optimizer statistics during each client update, with an SVRG-style correction in Mime, to approximate centralized optimization while reducing client drift. Distinct from server-only FedOpt and persistent-client SCAFFOLD. Inherits variance reduction and centralized momentum/adaptive optimizers. The base method must decompose into the specified update/statistics maps; theory uses bounded gradient/Hessian dissimilarity and smoothness regimes. Mimicking does not mean identical centralized trajectories or guarantees for every optimizer, and extra full local-gradient work has a cost.", "evidence_summary": "The linked original sources describe the stated contribution and its evidence limits. WRS has not independently rerun the experiments or certified a proof. Distinct from server-only FedOpt and persistent-client SCAFFOLD. Inherits variance reduction and centralized momentum/adaptive optimizers. The base method must decompose into the specified update/statistics maps; theory uses bounded gradient/Hessian dissimilarity and smoothness regimes. Mimicking does not mean identical centralized trajectories or guarantees for every optimizer, and extra full local-gradient work has a cost.", "references": [ { "title": "Mime: Mimicking Centralized Stochastic Algorithms in Federated Learning", "url": "https://arxiv.org/abs/2008.03606v1", "locator": "Original contribution and stated assumptions, evaluation scope and limitations; source-linked assessment, not independent execution or proof certification." } ], "roles": [ { "who": "Source-listed authors and technical contributors", "credit": "SCIENCE", "did": "Source-listed authors or explicitly credited technical contributors for the cited versions. Family entries retain source-specific attribution; a combined list is not one original-paper byline or equal individual work. Names alone do not establish resolved identities." } ], "target": { "record_id": "urn:uuid:509fbfe1-fabd-441d-afca-63761f06264b", "version": 1, "content_hash": "sha256:90bfadd81c07269264790a2154af8de2a89a0996c07e5caeec11f4a3d6ef3e6d" }, "scientific_correctness_certified": false, "independent_expert_calibration": false, "method": "PROPRIETARY", "points": "110000" }, "canonical_agent_submission": false, "scientific_verification_awarded": false, "reader_guide": null, "reading_status": "SOURCE_LINKED_NOTE" }