{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2024:4ESHQNMIQNMRBDURQ75ZIQMM7J","short_pith_number":"pith:4ESHQNMI","schema_version":"1.0","canonical_sha256":"e1247835888359108e9187fb94418cfa473d8edf15e9d9d5a0336fef784715ca","source":{"kind":"arxiv","id":"2401.01728","version":2},"attestation_state":"computed","paper":{"title":"Ravnest: Decentralized Asynchronous Training on Heterogeneous Devices","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.AI","cs.DC"],"primary_cat":"cs.LG","authors_text":"Anirudh Rajiv Menon, Kailash Ahirwar, Unnikrishnan Menon","submitted_at":"2024-01-03T13:07:07Z","abstract_excerpt":"Modern deep learning models, growing larger and more complex, have demonstrated exceptional generalization and accuracy due to training on huge datasets. This trend is expected to continue. However, the increasing size of these models poses challenges in training, as traditional centralized methods are limited by memory constraints at such scales. This paper proposes an asynchronous decentralized training paradigm for large modern deep learning models that harnesses the compute power of regular heterogeneous PCs with limited resources connected across the internet to achieve favourable perform"},"verification_status":{"content_addressed":true,"pith_receipt":true,"author_attested":false,"weak_author_claims":0,"strong_author_claims":0,"externally_anchored":false,"storage_verified":false,"citation_signatures":0,"replication_records":0,"graph_snapshot":true,"references_resolved":false,"formal_links_present":false},"canonical_record":{"source":{"id":"2401.01728","kind":"arxiv","version":2},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2024-01-03T13:07:07Z","cross_cats_sorted":["cs.AI","cs.DC"],"title_canon_sha256":"f83a9fbd6f12ae155c63ec2ad227235526a4b88e4a43ba018b35b84cb85e84ad","abstract_canon_sha256":"2c7bc298f75f2fe56d7f6e5b350d39ba2a913b59a568a571cb4a8409007fca6c"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T08:21:46.964467Z","signature_b64":"l4ofroEOC0O5scaxiUWvDJJ01l/7AejAmsn/w7S4fF58BH7AHvw3j+RaY5jAKMUR1jAkm461Cha5f8DgoYwFCg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"e1247835888359108e9187fb94418cfa473d8edf15e9d9d5a0336fef784715ca","last_reissued_at":"2026-07-05T08:21:46.963975Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T08:21:46.963975Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Ravnest: Decentralized Asynchronous Training on Heterogeneous Devices","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.AI","cs.DC"],"primary_cat":"cs.LG","authors_text":"Anirudh Rajiv Menon, Kailash Ahirwar, Unnikrishnan Menon","submitted_at":"2024-01-03T13:07:07Z","abstract_excerpt":"Modern deep learning models, growing larger and more complex, have demonstrated exceptional generalization and accuracy due to training on huge datasets. This trend is expected to continue. However, the increasing size of these models poses challenges in training, as traditional centralized methods are limited by memory constraints at such scales. This paper proposes an asynchronous decentralized training paradigm for large modern deep learning models that harnesses the compute power of regular heterogeneous PCs with limited resources connected across the internet to achieve favourable perform"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2401.01728","kind":"arxiv","version":2},"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/2401.01728/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"},"aliases":[{"alias_kind":"arxiv","alias_value":"2401.01728","created_at":"2026-07-05T08:21:46.964029+00:00"},{"alias_kind":"arxiv_version","alias_value":"2401.01728v2","created_at":"2026-07-05T08:21:46.964029+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2401.01728","created_at":"2026-07-05T08:21:46.964029+00:00"},{"alias_kind":"pith_short_12","alias_value":"4ESHQNMIQNMR","created_at":"2026-07-05T08:21:46.964029+00:00"},{"alias_kind":"pith_short_16","alias_value":"4ESHQNMIQNMRBDUR","created_at":"2026-07-05T08:21:46.964029+00:00"},{"alias_kind":"pith_short_8","alias_value":"4ESHQNMI","created_at":"2026-07-05T08:21:46.964029+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":0,"sample":[{"citing_arxiv_id":"2606.24722","citing_title":"Decentralised AI Training and Inference with BlockTrain","ref_index":107,"is_internal_anchor":false}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/4ESHQNMIQNMRBDURQ75ZIQMM7J","json":"https://pith.science/pith/4ESHQNMIQNMRBDURQ75ZIQMM7J.json","graph_json":"https://pith.science/api/pith-number/4ESHQNMIQNMRBDURQ75ZIQMM7J/graph.json","events_json":"https://pith.science/api/pith-number/4ESHQNMIQNMRBDURQ75ZIQMM7J/events.json","paper":"https://pith.science/paper/4ESHQNMI"},"agent_actions":{"view_html":"https://pith.science/pith/4ESHQNMIQNMRBDURQ75ZIQMM7J","download_json":"https://pith.science/pith/4ESHQNMIQNMRBDURQ75ZIQMM7J.json","view_paper":"https://pith.science/paper/4ESHQNMI","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2401.01728&json=true","fetch_graph":"https://pith.science/api/pith-number/4ESHQNMIQNMRBDURQ75ZIQMM7J/graph.json","fetch_events":"https://pith.science/api/pith-number/4ESHQNMIQNMRBDURQ75ZIQMM7J/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/4ESHQNMIQNMRBDURQ75ZIQMM7J/action/timestamp_anchor","attest_storage":"https://pith.science/pith/4ESHQNMIQNMRBDURQ75ZIQMM7J/action/storage_attestation","attest_author":"https://pith.science/pith/4ESHQNMIQNMRBDURQ75ZIQMM7J/action/author_attestation","sign_citation":"https://pith.science/pith/4ESHQNMIQNMRBDURQ75ZIQMM7J/action/citation_signature","submit_replication":"https://pith.science/pith/4ESHQNMIQNMRBDURQ75ZIQMM7J/action/replication_record"}},"created_at":"2026-07-05T08:21:46.964029+00:00","updated_at":"2026-07-05T08:21:46.964029+00:00"}