{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2025:K2G7HXOKB5SN7VCWSGSWRPBTUP","short_pith_number":"pith:K2G7HXOK","canonical_record":{"source":{"id":"2506.12425","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.DC","submitted_at":"2025-06-14T09:52:24Z","cross_cats_sorted":["cs.LG"],"title_canon_sha256":"95334acb719ed8e7c6ef6feb5574d3d1d13582c74325aa11cb90bae6739adb39","abstract_canon_sha256":"381e3cd8793b36517fac98be15bb932306056f546ae80d2935eaedc8f2ae1431"},"schema_version":"1.0"},"canonical_sha256":"568df3ddca0f64dfd45691a568bc33a3ca5c8c79c232047b144a9c4c320fca78","source":{"kind":"arxiv","id":"2506.12425","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2506.12425","created_at":"2026-07-05T11:21:53Z"},{"alias_kind":"arxiv_version","alias_value":"2506.12425v1","created_at":"2026-07-05T11:21:53Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2506.12425","created_at":"2026-07-05T11:21:53Z"},{"alias_kind":"pith_short_12","alias_value":"K2G7HXOKB5SN","created_at":"2026-07-05T11:21:53Z"},{"alias_kind":"pith_short_16","alias_value":"K2G7HXOKB5SN7VCW","created_at":"2026-07-05T11:21:53Z"},{"alias_kind":"pith_short_8","alias_value":"K2G7HXOK","created_at":"2026-07-05T11:21:53Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2025:K2G7HXOKB5SN7VCWSGSWRPBTUP","target":"record","payload":{"canonical_record":{"source":{"id":"2506.12425","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.DC","submitted_at":"2025-06-14T09:52:24Z","cross_cats_sorted":["cs.LG"],"title_canon_sha256":"95334acb719ed8e7c6ef6feb5574d3d1d13582c74325aa11cb90bae6739adb39","abstract_canon_sha256":"381e3cd8793b36517fac98be15bb932306056f546ae80d2935eaedc8f2ae1431"},"schema_version":"1.0"},"canonical_sha256":"568df3ddca0f64dfd45691a568bc33a3ca5c8c79c232047b144a9c4c320fca78","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T11:21:53.139498Z","signature_b64":"3wY8eVJrMpNevzNyTFyWggwtaXFpqbbdUHDTf0voX1alV//eP7a7q67oO7pjw5J2i4pO+tMSzFxoL5TG2bbiBw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"568df3ddca0f64dfd45691a568bc33a3ca5c8c79c232047b144a9c4c320fca78","last_reissued_at":"2026-07-05T11:21:53.139051Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T11:21:53.139051Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2506.12425","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":"2026-07-05T11:21:53Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"l9GqwtI1+YXNFo/z5pZpIJxEPWaKXKQfziXkkCPALUMk7D15KwohP1afVngwyRu5hpuTK2qAZ1bNKEYTcTOMCA==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-08T00:37:25.812152Z"},"content_sha256":"7320f766af6f05bba5534d17d74064ffc1e904024fd6919e0dbf282907be77ed","schema_version":"1.0","event_id":"sha256:7320f766af6f05bba5534d17d74064ffc1e904024fd6919e0dbf282907be77ed"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2025:K2G7HXOKB5SN7VCWSGSWRPBTUP","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Optimizing Federated Learning using Remote Embeddings for Graph Neural Networks","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.LG"],"primary_cat":"cs.DC","authors_text":"Pranjal Naman, Yogesh Simmhan","submitted_at":"2025-06-14T09:52:24Z","abstract_excerpt":"Graph Neural Networks (GNNs) have experienced rapid advancements in recent years due to their ability to learn meaningful representations from graph data structures. Federated Learning (FL) has emerged as a viable machine learning approach for training a shared model on decentralized data, addressing privacy concerns while leveraging parallelism. Existing methods that address the unique requirements of federated GNN training using remote embeddings to enhance convergence accuracy are limited by their diminished performance due to large communication costs with a shared embedding server. In thi"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2506.12425","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/2506.12425/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":"2026-07-05T11:21:53Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"W7AT6hs5XawC39JPSEikRhALbmCdfJ92UGUo/0ew3u/hCwYDBnXg4x0eAYpOBnDaS6FawtrscfOgPtWPSXXADw==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-08T00:37:25.812514Z"},"content_sha256":"d7fe1f402b22b7b58556625ac9e0a72b054f1f5a498c85e0cc7ae5e38d540560","schema_version":"1.0","event_id":"sha256:d7fe1f402b22b7b58556625ac9e0a72b054f1f5a498c85e0cc7ae5e38d540560"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/K2G7HXOKB5SN7VCWSGSWRPBTUP/bundle.json","state_url":"https://pith.science/pith/K2G7HXOKB5SN7VCWSGSWRPBTUP/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/K2G7HXOKB5SN7VCWSGSWRPBTUP/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":"2026-08-08T00:37:25Z","links":{"resolver":"https://pith.science/pith/K2G7HXOKB5SN7VCWSGSWRPBTUP","bundle":"https://pith.science/pith/K2G7HXOKB5SN7VCWSGSWRPBTUP/bundle.json","state":"https://pith.science/pith/K2G7HXOKB5SN7VCWSGSWRPBTUP/state.json","well_known_bundle":"https://pith.science/.well-known/pith/K2G7HXOKB5SN7VCWSGSWRPBTUP/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2025:K2G7HXOKB5SN7VCWSGSWRPBTUP","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":"381e3cd8793b36517fac98be15bb932306056f546ae80d2935eaedc8f2ae1431","cross_cats_sorted":["cs.LG"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.DC","submitted_at":"2025-06-14T09:52:24Z","title_canon_sha256":"95334acb719ed8e7c6ef6feb5574d3d1d13582c74325aa11cb90bae6739adb39"},"schema_version":"1.0","source":{"id":"2506.12425","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2506.12425","created_at":"2026-07-05T11:21:53Z"},{"alias_kind":"arxiv_version","alias_value":"2506.12425v1","created_at":"2026-07-05T11:21:53Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2506.12425","created_at":"2026-07-05T11:21:53Z"},{"alias_kind":"pith_short_12","alias_value":"K2G7HXOKB5SN","created_at":"2026-07-05T11:21:53Z"},{"alias_kind":"pith_short_16","alias_value":"K2G7HXOKB5SN7VCW","created_at":"2026-07-05T11:21:53Z"},{"alias_kind":"pith_short_8","alias_value":"K2G7HXOK","created_at":"2026-07-05T11:21:53Z"}],"graph_snapshots":[{"event_id":"sha256:d7fe1f402b22b7b58556625ac9e0a72b054f1f5a498c85e0cc7ae5e38d540560","target":"graph","created_at":"2026-07-05T11:21:53Z","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/2506.12425/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Graph Neural Networks (GNNs) have experienced rapid advancements in recent years due to their ability to learn meaningful representations from graph data structures. Federated Learning (FL) has emerged as a viable machine learning approach for training a shared model on decentralized data, addressing privacy concerns while leveraging parallelism. Existing methods that address the unique requirements of federated GNN training using remote embeddings to enhance convergence accuracy are limited by their diminished performance due to large communication costs with a shared embedding server. In thi","authors_text":"Pranjal Naman, Yogesh Simmhan","cross_cats":["cs.LG"],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.DC","submitted_at":"2025-06-14T09:52:24Z","title":"Optimizing Federated Learning using Remote Embeddings for Graph Neural Networks"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2506.12425","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":"sha256:7320f766af6f05bba5534d17d74064ffc1e904024fd6919e0dbf282907be77ed","target":"record","created_at":"2026-07-05T11:21:53Z","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":"381e3cd8793b36517fac98be15bb932306056f546ae80d2935eaedc8f2ae1431","cross_cats_sorted":["cs.LG"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.DC","submitted_at":"2025-06-14T09:52:24Z","title_canon_sha256":"95334acb719ed8e7c6ef6feb5574d3d1d13582c74325aa11cb90bae6739adb39"},"schema_version":"1.0","source":{"id":"2506.12425","kind":"arxiv","version":1}},"canonical_sha256":"568df3ddca0f64dfd45691a568bc33a3ca5c8c79c232047b144a9c4c320fca78","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"568df3ddca0f64dfd45691a568bc33a3ca5c8c79c232047b144a9c4c320fca78","first_computed_at":"2026-07-05T11:21:53.139051Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T11:21:53.139051Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"3wY8eVJrMpNevzNyTFyWggwtaXFpqbbdUHDTf0voX1alV//eP7a7q67oO7pjw5J2i4pO+tMSzFxoL5TG2bbiBw==","signature_status":"signed_v1","signed_at":"2026-07-05T11:21:53.139498Z","signed_message":"canonical_sha256_bytes"},"source_id":"2506.12425","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:7320f766af6f05bba5534d17d74064ffc1e904024fd6919e0dbf282907be77ed","sha256:d7fe1f402b22b7b58556625ac9e0a72b054f1f5a498c85e0cc7ae5e38d540560"],"state_sha256":"9192a64759f57a404251d8b8b2bbf3e19162def3c7f34d511c1b540ff987e487"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"VyU3s/W617HTjG0BWEd6syRd/K7ZFWfkgnwv/5AoYW/40SJNgVRFO8cAEaiYE7iSNFtAVCC/YLoC9ixkASAbBQ==","signed_message":"bundle_sha256_bytes","signed_at":"2026-08-08T00:37:25.814851Z","bundle_sha256":"c388055457eb6a4f05e75b5ded2a612e0cdf4bd519f2a96da8239a272ac54d39"}}