{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2025:DQWXPC2GFCOSN4UOOVWPNYWMBB","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":"cbe76c416d7b3d6b115862405519c84c0c04880be0baa6a60e5431944fbcf258","cross_cats_sorted":["cs.DC"],"license":"http://creativecommons.org/licenses/by-sa/4.0/","primary_cat":"cs.LG","submitted_at":"2025-02-12T15:51:39Z","title_canon_sha256":"0d98dae4bb77467d705f440d4c44a1bdeae207e844b8131d189f2b45a0ddf298"},"schema_version":"1.0","source":{"id":"2502.08692","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2502.08692","created_at":"2026-07-05T10:13:28Z"},{"alias_kind":"arxiv_version","alias_value":"2502.08692v1","created_at":"2026-07-05T10:13:28Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2502.08692","created_at":"2026-07-05T10:13:28Z"},{"alias_kind":"pith_short_12","alias_value":"DQWXPC2GFCOS","created_at":"2026-07-05T10:13:28Z"},{"alias_kind":"pith_short_16","alias_value":"DQWXPC2GFCOSN4UO","created_at":"2026-07-05T10:13:28Z"},{"alias_kind":"pith_short_8","alias_value":"DQWXPC2G","created_at":"2026-07-05T10:13:28Z"}],"graph_snapshots":[{"event_id":"sha256:e1c0c473619ffe9c47dfccfd75270e1b28f266e238e4dfbb6b1ca1319f347c04","target":"graph","created_at":"2026-07-05T10:13:28Z","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/2502.08692/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Split Learning (SL) recently emerged as an efficient paradigm for distributed Machine Learning (ML) suitable for the Internet Of Things (IoT)-Cloud systems. However, deploying SL on resource-constrained edge IoT platforms poses a significant challenge in terms of balancing the model performance against the processing, memory, and energy resources. In this work, we present a practical study of deploying SL framework on a real-world Field-Programmable Gate Array (FPGA)-based edge IoT platform. We address the SL framework applied to a time-series processing model based on Recurrent Neural Network","authors_text":"Dejan Vukobratovic, Marco Zennaro, Maria Liz Crespo, Romina Soledad Molina, Vukan Ninkovic","cross_cats":["cs.DC"],"headline":"","license":"http://creativecommons.org/licenses/by-sa/4.0/","primary_cat":"cs.LG","submitted_at":"2025-02-12T15:51:39Z","title":"Efficient Split Learning LSTM Models for FPGA-based Edge IoT Devices"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2502.08692","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":"sha256:65362b0e579826be8c66f329a0fa9e3b2681c123c0fb7710b53141b75e435305","target":"record","created_at":"2026-07-05T10:13:28Z","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":"cbe76c416d7b3d6b115862405519c84c0c04880be0baa6a60e5431944fbcf258","cross_cats_sorted":["cs.DC"],"license":"http://creativecommons.org/licenses/by-sa/4.0/","primary_cat":"cs.LG","submitted_at":"2025-02-12T15:51:39Z","title_canon_sha256":"0d98dae4bb77467d705f440d4c44a1bdeae207e844b8131d189f2b45a0ddf298"},"schema_version":"1.0","source":{"id":"2502.08692","kind":"arxiv","version":1}},"canonical_sha256":"1c2d778b46289d26f28e756cf6e2cc08444d911bc64dfe20d4dec5f94fadc5f6","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"1c2d778b46289d26f28e756cf6e2cc08444d911bc64dfe20d4dec5f94fadc5f6","first_computed_at":"2026-07-05T10:13:28.702513Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T10:13:28.702513Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"l7xgdxTPiMfL7eDDYJ1lyoJ0PgfsPMB81DR6Q0P54AKrt6aAiN65Uoz2DvD4EKkvve2qIzmlw8L+2Y1LixrFBA==","signature_status":"signed_v1","signed_at":"2026-07-05T10:13:28.703098Z","signed_message":"canonical_sha256_bytes"},"source_id":"2502.08692","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:65362b0e579826be8c66f329a0fa9e3b2681c123c0fb7710b53141b75e435305","sha256:e1c0c473619ffe9c47dfccfd75270e1b28f266e238e4dfbb6b1ca1319f347c04"],"state_sha256":"fa4b31718b2496110e73f2af6d3216129feb4f2cf22e14fd7fb4a608570fbfaf"}