{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2021:G5NKI347J76ZFV53C43WEXLJRY","short_pith_number":"pith:G5NKI347","canonical_record":{"source":{"id":"2112.02543","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2021-12-05T11:17:17Z","cross_cats_sorted":["cs.DC"],"title_canon_sha256":"7261a6f73a900b550f6dd841d4d000c6b069f7b873bf1d4a500475bc72aea242","abstract_canon_sha256":"b16dedb9d74938eedd29055975da56079100077ab1493946cf0f4e4821d85687"},"schema_version":"1.0"},"canonical_sha256":"375aa46f9f4ffd92d7bb1737625d698e158dd370c0f47d9a3d0c5309ebad387c","source":{"kind":"arxiv","id":"2112.02543","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2112.02543","created_at":"2026-07-05T03:37:54Z"},{"alias_kind":"arxiv_version","alias_value":"2112.02543v1","created_at":"2026-07-05T03:37:54Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2112.02543","created_at":"2026-07-05T03:37:54Z"},{"alias_kind":"pith_short_12","alias_value":"G5NKI347J76Z","created_at":"2026-07-05T03:37:54Z"},{"alias_kind":"pith_short_16","alias_value":"G5NKI347J76ZFV53","created_at":"2026-07-05T03:37:54Z"},{"alias_kind":"pith_short_8","alias_value":"G5NKI347","created_at":"2026-07-05T03:37:54Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2021:G5NKI347J76ZFV53C43WEXLJRY","target":"record","payload":{"canonical_record":{"source":{"id":"2112.02543","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2021-12-05T11:17:17Z","cross_cats_sorted":["cs.DC"],"title_canon_sha256":"7261a6f73a900b550f6dd841d4d000c6b069f7b873bf1d4a500475bc72aea242","abstract_canon_sha256":"b16dedb9d74938eedd29055975da56079100077ab1493946cf0f4e4821d85687"},"schema_version":"1.0"},"canonical_sha256":"375aa46f9f4ffd92d7bb1737625d698e158dd370c0f47d9a3d0c5309ebad387c","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T03:37:54.253417Z","signature_b64":"fMXK3Uh/KetQhIyx59NN06CvBsiUNf8OeYG0hIF1wWWmg8h8ulwRhQeVlCtAoyaOZqLSw0xjB52V3z3CbupPCQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"375aa46f9f4ffd92d7bb1737625d698e158dd370c0f47d9a3d0c5309ebad387c","last_reissued_at":"2026-07-05T03:37:54.253000Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T03:37:54.253000Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2112.02543","source_version":1,"attestation_state":"computed"},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-07-05T03:37:54Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"UVHejOVMqUpu97bPBeVP5k80xIpVFSLPU3yx2e27We/hOj6KEkQbg0S5btvzRSamvgRlampm9ze+kfxVMaUrAQ==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-21T18:43:29.219230Z"},"content_sha256":"24e49f62165cd6feb103d7ddd70eac50ee468b7a6d3ab15848a21562d677f113","schema_version":"1.0","event_id":"sha256:24e49f62165cd6feb103d7ddd70eac50ee468b7a6d3ab15848a21562d677f113"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2021:G5NKI347J76ZFV53C43WEXLJRY","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Joint Superposition Coding and Training for Federated Learning over Multi-Width Neural Networks","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.DC"],"primary_cat":"cs.LG","authors_text":"Hankyul Baek, Jihong Park, Joongheon Kim, Mehdi Bennis, Mingyue Ji, Soyi Jung, Won Joon Yun, Yunseok Kwak","submitted_at":"2021-12-05T11:17:17Z","abstract_excerpt":"This paper aims to integrate two synergetic technologies, federated learning (FL) and width-adjustable slimmable neural network (SNN) architectures. FL preserves data privacy by exchanging the locally trained models of mobile devices. By adopting SNNs as local models, FL can flexibly cope with the time-varying energy capacities of mobile devices. Combining FL and SNNs is however non-trivial, particularly under wireless connections with time-varying channel conditions. Furthermore, existing multi-width SNN training algorithms are sensitive to the data distributions across devices, so are ill-su"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2112.02543","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/2112.02543/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"},"verdict_id":null},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-07-05T03:37:54Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"+uyjJ7pXUVP2kGJpbLYoovjbhRTc/9c3c83e4uEIrRPoHA/RDMYQZvDudjwmfakFGasGWCJ6qL4uWL0B2onQBQ==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-21T18:43:29.219610Z"},"content_sha256":"93005cec4d87d33759e2f52c6829c09be04540452cf1a2ef4683f74dc4009cf8","schema_version":"1.0","event_id":"sha256:93005cec4d87d33759e2f52c6829c09be04540452cf1a2ef4683f74dc4009cf8"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/G5NKI347J76ZFV53C43WEXLJRY/bundle.json","state_url":"https://pith.science/pith/G5NKI347J76ZFV53C43WEXLJRY/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/G5NKI347J76ZFV53C43WEXLJRY/bundle.json","status":"primary"}],"public_keys":[{"key_id":"pith-v1-2026-05","algorithm":"ed25519","format":"raw","public_key_b64":"stVStoiQhXFxp4s2pdzPNoqVNBMojDU/fJ2db5S3CbM=","public_key_hex":"b2d552b68890857171a78b36a5dccf368a953413288c353f7c9d9d6f94b709b3","fingerprint_sha256_b32_first128bits":"RVFV5Z2OI2J3ZUO7ERDEBCYNKS","fingerprint_sha256_hex":"8d4b5ee74e4693bcd1df2446408b0d54","rotates_at":null,"url":"https://pith.science/pith-signing-key.json","notes":"Pith uses this Ed25519 key to sign canonical record SHA-256 digests. Verify with: ed25519_verify(public_key, message=canonical_sha256_bytes, signature=base64decode(signature_b64))."}],"merge_version":"pith-open-graph-merge-v1","built_at":"2026-08-21T18:43:29Z","links":{"resolver":"https://pith.science/pith/G5NKI347J76ZFV53C43WEXLJRY","bundle":"https://pith.science/pith/G5NKI347J76ZFV53C43WEXLJRY/bundle.json","state":"https://pith.science/pith/G5NKI347J76ZFV53C43WEXLJRY/state.json","well_known_bundle":"https://pith.science/.well-known/pith/G5NKI347J76ZFV53C43WEXLJRY/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2021:G5NKI347J76ZFV53C43WEXLJRY","merge_version":"pith-open-graph-merge-v1","event_count":2,"valid_event_count":2,"invalid_event_count":0,"equivocation_count":0,"current":{"canonical_record":{"metadata":{"abstract_canon_sha256":"b16dedb9d74938eedd29055975da56079100077ab1493946cf0f4e4821d85687","cross_cats_sorted":["cs.DC"],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2021-12-05T11:17:17Z","title_canon_sha256":"7261a6f73a900b550f6dd841d4d000c6b069f7b873bf1d4a500475bc72aea242"},"schema_version":"1.0","source":{"id":"2112.02543","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2112.02543","created_at":"2026-07-05T03:37:54Z"},{"alias_kind":"arxiv_version","alias_value":"2112.02543v1","created_at":"2026-07-05T03:37:54Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2112.02543","created_at":"2026-07-05T03:37:54Z"},{"alias_kind":"pith_short_12","alias_value":"G5NKI347J76Z","created_at":"2026-07-05T03:37:54Z"},{"alias_kind":"pith_short_16","alias_value":"G5NKI347J76ZFV53","created_at":"2026-07-05T03:37:54Z"},{"alias_kind":"pith_short_8","alias_value":"G5NKI347","created_at":"2026-07-05T03:37:54Z"}],"graph_snapshots":[{"event_id":"sha256:93005cec4d87d33759e2f52c6829c09be04540452cf1a2ef4683f74dc4009cf8","target":"graph","created_at":"2026-07-05T03:37:54Z","signer":{"key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signer_id":"pith.science","signer_type":"pith_registry"},"payload":{"graph_snapshot":{"author_claims":{"count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57","strong_count":0},"builder_version":"pith-number-builder-2026-05-17-v1","claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"formal_canon":{"evidence_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"integrity":{"available":true,"clean":true,"detectors_run":[],"endpoint":"/pith/2112.02543/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"This paper aims to integrate two synergetic technologies, federated learning (FL) and width-adjustable slimmable neural network (SNN) architectures. FL preserves data privacy by exchanging the locally trained models of mobile devices. By adopting SNNs as local models, FL can flexibly cope with the time-varying energy capacities of mobile devices. Combining FL and SNNs is however non-trivial, particularly under wireless connections with time-varying channel conditions. Furthermore, existing multi-width SNN training algorithms are sensitive to the data distributions across devices, so are ill-su","authors_text":"Hankyul Baek, Jihong Park, Joongheon Kim, Mehdi Bennis, Mingyue Ji, Soyi Jung, Won Joon Yun, Yunseok Kwak","cross_cats":["cs.DC"],"headline":"","license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2021-12-05T11:17:17Z","title":"Joint Superposition Coding and Training for Federated Learning over Multi-Width Neural Networks"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2112.02543","kind":"arxiv","version":1},"verdict":{"created_at":null,"id":null,"model_set":{},"one_line_summary":"","pipeline_version":null,"pith_extraction_headline":"","strongest_claim":"","weakest_assumption":""}},"verdict_id":null}}],"author_attestations":[],"timestamp_anchors":[],"storage_attestations":[],"citation_signatures":[],"replication_records":[],"corrections":[],"mirror_hints":[],"record_created":{"event_id":"sha256:24e49f62165cd6feb103d7ddd70eac50ee468b7a6d3ab15848a21562d677f113","target":"record","created_at":"2026-07-05T03:37:54Z","signer":{"key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signer_id":"pith.science","signer_type":"pith_registry"},"payload":{"attestation_state":"computed","canonical_record":{"metadata":{"abstract_canon_sha256":"b16dedb9d74938eedd29055975da56079100077ab1493946cf0f4e4821d85687","cross_cats_sorted":["cs.DC"],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2021-12-05T11:17:17Z","title_canon_sha256":"7261a6f73a900b550f6dd841d4d000c6b069f7b873bf1d4a500475bc72aea242"},"schema_version":"1.0","source":{"id":"2112.02543","kind":"arxiv","version":1}},"canonical_sha256":"375aa46f9f4ffd92d7bb1737625d698e158dd370c0f47d9a3d0c5309ebad387c","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"375aa46f9f4ffd92d7bb1737625d698e158dd370c0f47d9a3d0c5309ebad387c","first_computed_at":"2026-07-05T03:37:54.253000Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T03:37:54.253000Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"fMXK3Uh/KetQhIyx59NN06CvBsiUNf8OeYG0hIF1wWWmg8h8ulwRhQeVlCtAoyaOZqLSw0xjB52V3z3CbupPCQ==","signature_status":"signed_v1","signed_at":"2026-07-05T03:37:54.253417Z","signed_message":"canonical_sha256_bytes"},"source_id":"2112.02543","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:24e49f62165cd6feb103d7ddd70eac50ee468b7a6d3ab15848a21562d677f113","sha256:93005cec4d87d33759e2f52c6829c09be04540452cf1a2ef4683f74dc4009cf8"],"state_sha256":"46617828050f95f4f310718cbd6e84e04a18028e0bd975726cab85a839b53b8c"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"0JKXGpQjpF2mW4y0MCCv5d4xtjdQj91DRsXlUPsKTsiAVRw2dgQf61IPqUkmjCFZmUJ5zgDF+kCYcNtscpxiBQ==","signed_message":"bundle_sha256_bytes","signed_at":"2026-08-21T18:43:29.223284Z","bundle_sha256":"d22299910660f4a91fb34ac0e951e1ffebf82e141d2cb489df2fca3b1c5da47e"}}