{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2024:LSNW4TUJAYDU2H2WEPQWF6ZDOC","short_pith_number":"pith:LSNW4TUJ","canonical_record":{"source":{"id":"2412.04081","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2024-12-05T11:32:14Z","cross_cats_sorted":["cs.AI"],"title_canon_sha256":"6dd217c2336c0dbcb1fdc66ec9c90cc8e2d93498970902f95db49c5274ec5008","abstract_canon_sha256":"d4a9f53ca77ff2e2618c3d3b42ff05182324c5c90a3ad6ec4d20b2adf6d33d9a"},"schema_version":"1.0"},"canonical_sha256":"5c9b6e4e8906074d1f5623e162fb2370b38504e8c9f8098c3693b5bfd5c6c736","source":{"kind":"arxiv","id":"2412.04081","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2412.04081","created_at":"2026-07-05T09:44:58Z"},{"alias_kind":"arxiv_version","alias_value":"2412.04081v1","created_at":"2026-07-05T09:44:58Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2412.04081","created_at":"2026-07-05T09:44:58Z"},{"alias_kind":"pith_short_12","alias_value":"LSNW4TUJAYDU","created_at":"2026-07-05T09:44:58Z"},{"alias_kind":"pith_short_16","alias_value":"LSNW4TUJAYDU2H2W","created_at":"2026-07-05T09:44:58Z"},{"alias_kind":"pith_short_8","alias_value":"LSNW4TUJ","created_at":"2026-07-05T09:44:58Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2024:LSNW4TUJAYDU2H2WEPQWF6ZDOC","target":"record","payload":{"canonical_record":{"source":{"id":"2412.04081","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2024-12-05T11:32:14Z","cross_cats_sorted":["cs.AI"],"title_canon_sha256":"6dd217c2336c0dbcb1fdc66ec9c90cc8e2d93498970902f95db49c5274ec5008","abstract_canon_sha256":"d4a9f53ca77ff2e2618c3d3b42ff05182324c5c90a3ad6ec4d20b2adf6d33d9a"},"schema_version":"1.0"},"canonical_sha256":"5c9b6e4e8906074d1f5623e162fb2370b38504e8c9f8098c3693b5bfd5c6c736","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T09:44:58.869396Z","signature_b64":"HHCB3O2nRXuxWj8JRwPYKcADyyRdG9W50/eraXS020YkF2juq0FyQ7MKC158oGdxvUoqDNCa/d2bqGuLbEznCA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"5c9b6e4e8906074d1f5623e162fb2370b38504e8c9f8098c3693b5bfd5c6c736","last_reissued_at":"2026-07-05T09:44:58.868910Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T09:44:58.868910Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2412.04081","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":"2026-07-05T09:44:58Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"cByBkMR048Y9ht3R8sN0qW/K9gM/FLv3rVx4VO0AlAozkg6uZgVqqt03UBgY+uwQn67dY5ApkwkqTLLpkpIdCA==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-14T12:36:15.209272Z"},"content_sha256":"05bc9103ec60a669c1325b92f73a14c65fc016940cc2ba59b5afd9c5d2e8e57c","schema_version":"1.0","event_id":"sha256:05bc9103ec60a669c1325b92f73a14c65fc016940cc2ba59b5afd9c5d2e8e57c"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2024:LSNW4TUJAYDU2H2WEPQWF6ZDOC","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Federated Learning in Mobile Networks: A Comprehensive Case Study on Traffic Forecasting","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.AI"],"primary_cat":"cs.LG","authors_text":"Francesc Wilhelmi, Marco Miozzo, Nikolaos Pavlidis, Paolo Dini, Pavlos S. Efraimidis, Pavol Mulinka, Remous-Aris Koutsiamanis, Selim F. Yilmaz, Vasileios Perifanis","submitted_at":"2024-12-05T11:32:14Z","abstract_excerpt":"The increasing demand for efficient resource allocation in mobile networks has catalyzed the exploration of innovative solutions that could enhance the task of real-time cellular traffic prediction. Under these circumstances, federated learning (FL) stands out as a distributed and privacy-preserving solution to foster collaboration among different sites, thus enabling responsive near-the-edge solutions. In this paper, we comprehensively study the potential benefits of FL in telecommunications through a case study on federated traffic forecasting using real-world data from base stations (BSs) i"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2412.04081","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/2412.04081/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":"2026-07-05T09:44:58Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"cFJzgPM4swf+6zVLh1zt3N3rVclkfqTMiIPPlYM90i59yMKWVhHgqXSCQcGXAQSR5RUXwTNrobnm3h+fiFtgCw==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-14T12:36:15.210172Z"},"content_sha256":"bae9b3d7e94b8a3dadcb010f94381518ee8b7adab65626a279386268402f8826","schema_version":"1.0","event_id":"sha256:bae9b3d7e94b8a3dadcb010f94381518ee8b7adab65626a279386268402f8826"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/LSNW4TUJAYDU2H2WEPQWF6ZDOC/bundle.json","state_url":"https://pith.science/pith/LSNW4TUJAYDU2H2WEPQWF6ZDOC/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/LSNW4TUJAYDU2H2WEPQWF6ZDOC/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":"2026-08-14T12:36:15Z","links":{"resolver":"https://pith.science/pith/LSNW4TUJAYDU2H2WEPQWF6ZDOC","bundle":"https://pith.science/pith/LSNW4TUJAYDU2H2WEPQWF6ZDOC/bundle.json","state":"https://pith.science/pith/LSNW4TUJAYDU2H2WEPQWF6ZDOC/state.json","well_known_bundle":"https://pith.science/.well-known/pith/LSNW4TUJAYDU2H2WEPQWF6ZDOC/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2024:LSNW4TUJAYDU2H2WEPQWF6ZDOC","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":"d4a9f53ca77ff2e2618c3d3b42ff05182324c5c90a3ad6ec4d20b2adf6d33d9a","cross_cats_sorted":["cs.AI"],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2024-12-05T11:32:14Z","title_canon_sha256":"6dd217c2336c0dbcb1fdc66ec9c90cc8e2d93498970902f95db49c5274ec5008"},"schema_version":"1.0","source":{"id":"2412.04081","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2412.04081","created_at":"2026-07-05T09:44:58Z"},{"alias_kind":"arxiv_version","alias_value":"2412.04081v1","created_at":"2026-07-05T09:44:58Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2412.04081","created_at":"2026-07-05T09:44:58Z"},{"alias_kind":"pith_short_12","alias_value":"LSNW4TUJAYDU","created_at":"2026-07-05T09:44:58Z"},{"alias_kind":"pith_short_16","alias_value":"LSNW4TUJAYDU2H2W","created_at":"2026-07-05T09:44:58Z"},{"alias_kind":"pith_short_8","alias_value":"LSNW4TUJ","created_at":"2026-07-05T09:44:58Z"}],"graph_snapshots":[{"event_id":"sha256:bae9b3d7e94b8a3dadcb010f94381518ee8b7adab65626a279386268402f8826","target":"graph","created_at":"2026-07-05T09:44:58Z","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/2412.04081/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"The increasing demand for efficient resource allocation in mobile networks has catalyzed the exploration of innovative solutions that could enhance the task of real-time cellular traffic prediction. Under these circumstances, federated learning (FL) stands out as a distributed and privacy-preserving solution to foster collaboration among different sites, thus enabling responsive near-the-edge solutions. In this paper, we comprehensively study the potential benefits of FL in telecommunications through a case study on federated traffic forecasting using real-world data from base stations (BSs) i","authors_text":"Francesc Wilhelmi, Marco Miozzo, Nikolaos Pavlidis, Paolo Dini, Pavlos S. Efraimidis, Pavol Mulinka, Remous-Aris Koutsiamanis, Selim F. Yilmaz, Vasileios Perifanis","cross_cats":["cs.AI"],"headline":"","license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2024-12-05T11:32:14Z","title":"Federated Learning in Mobile Networks: A Comprehensive Case Study on Traffic Forecasting"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2412.04081","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":"sha256:05bc9103ec60a669c1325b92f73a14c65fc016940cc2ba59b5afd9c5d2e8e57c","target":"record","created_at":"2026-07-05T09:44:58Z","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":"d4a9f53ca77ff2e2618c3d3b42ff05182324c5c90a3ad6ec4d20b2adf6d33d9a","cross_cats_sorted":["cs.AI"],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2024-12-05T11:32:14Z","title_canon_sha256":"6dd217c2336c0dbcb1fdc66ec9c90cc8e2d93498970902f95db49c5274ec5008"},"schema_version":"1.0","source":{"id":"2412.04081","kind":"arxiv","version":1}},"canonical_sha256":"5c9b6e4e8906074d1f5623e162fb2370b38504e8c9f8098c3693b5bfd5c6c736","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"5c9b6e4e8906074d1f5623e162fb2370b38504e8c9f8098c3693b5bfd5c6c736","first_computed_at":"2026-07-05T09:44:58.868910Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T09:44:58.868910Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"HHCB3O2nRXuxWj8JRwPYKcADyyRdG9W50/eraXS020YkF2juq0FyQ7MKC158oGdxvUoqDNCa/d2bqGuLbEznCA==","signature_status":"signed_v1","signed_at":"2026-07-05T09:44:58.869396Z","signed_message":"canonical_sha256_bytes"},"source_id":"2412.04081","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:05bc9103ec60a669c1325b92f73a14c65fc016940cc2ba59b5afd9c5d2e8e57c","sha256:bae9b3d7e94b8a3dadcb010f94381518ee8b7adab65626a279386268402f8826"],"state_sha256":"951d82cf050778c5f0066a3e8691963f5d350ea8330949b29451c9be98e0da82"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"ZmD/1uVGEt2/GZyuJriLaxa+K58J5oyzbQfjVvU6MwvtNsZfkwEFwsKlAd11kmAzkSFHXQjHfrH8AEgHANuaAQ==","signed_message":"bundle_sha256_bytes","signed_at":"2026-08-14T12:36:15.217411Z","bundle_sha256":"53effcf066f876da46d27d2d7761266ee561329de529001155dbbec44931506e"}}