{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2019:CDLELIOM2I6IRE56WJMEDMLFPV","short_pith_number":"pith:CDLELIOM","schema_version":"1.0","canonical_sha256":"10d645a1ccd23c8893beb25841b1657d461c9922190ebaf31d27b10e1a85ee11","source":{"kind":"arxiv","id":"1906.04329","version":1},"attestation_state":"computed","paper":{"title":"Federated Learning for Emoji Prediction in a Mobile Keyboard","license":"http://creativecommons.org/licenses/by-nc-sa/4.0/","headline":"","cross_cats":["cs.LG"],"primary_cat":"cs.CL","authors_text":"Fran\\c{c}oise Beaufays, Kanishka Rao, Rajiv Mathews, Swaroop Ramaswamy","submitted_at":"2019-06-11T00:40:33Z","abstract_excerpt":"We show that a word-level recurrent neural network can predict emoji from text typed on a mobile keyboard. We demonstrate the usefulness of transfer learning for predicting emoji by pretraining the model using a language modeling task. We also propose mechanisms to trigger emoji and tune the diversity of candidates. The model is trained using a distributed on-device learning framework called federated learning. The federated model is shown to achieve better performance than a server-trained model. This work demonstrates the feasibility of using federated learning to train production-quality mo"},"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":"1906.04329","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by-nc-sa/4.0/","primary_cat":"cs.CL","submitted_at":"2019-06-11T00:40:33Z","cross_cats_sorted":["cs.LG"],"title_canon_sha256":"29685e543ad62f2c82c5e57e7683272f45bc281430285cfa03af2101ea8d895c","abstract_canon_sha256":"69fc48769f5b12384bac515adc0f6ac849486521c3fc91c41c1024627f6dbdd4"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-17T23:43:39.647107Z","signature_b64":"nMJVDKlvRQ0WK7xcMoyi9hbIdhpeY3aehsxoQL0QosrDh0HHqsrAO0W2X4e6l5a10/z04cNRERKR0qlN9dB7Aw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"10d645a1ccd23c8893beb25841b1657d461c9922190ebaf31d27b10e1a85ee11","last_reissued_at":"2026-05-17T23:43:39.646490Z","signature_status":"signed_v1","first_computed_at":"2026-05-17T23:43:39.646490Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Federated Learning for Emoji Prediction in a Mobile Keyboard","license":"http://creativecommons.org/licenses/by-nc-sa/4.0/","headline":"","cross_cats":["cs.LG"],"primary_cat":"cs.CL","authors_text":"Fran\\c{c}oise Beaufays, Kanishka Rao, Rajiv Mathews, Swaroop Ramaswamy","submitted_at":"2019-06-11T00:40:33Z","abstract_excerpt":"We show that a word-level recurrent neural network can predict emoji from text typed on a mobile keyboard. We demonstrate the usefulness of transfer learning for predicting emoji by pretraining the model using a language modeling task. We also propose mechanisms to trigger emoji and tune the diversity of candidates. The model is trained using a distributed on-device learning framework called federated learning. The federated model is shown to achieve better performance than a server-trained model. This work demonstrates the feasibility of using federated learning to train production-quality mo"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1906.04329","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":""},"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":"1906.04329","created_at":"2026-05-17T23:43:39.646590+00:00"},{"alias_kind":"arxiv_version","alias_value":"1906.04329v1","created_at":"2026-05-17T23:43:39.646590+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1906.04329","created_at":"2026-05-17T23:43:39.646590+00:00"},{"alias_kind":"pith_short_12","alias_value":"CDLELIOM2I6I","created_at":"2026-05-18T12:33:12.712433+00:00"},{"alias_kind":"pith_short_16","alias_value":"CDLELIOM2I6IRE56","created_at":"2026-05-18T12:33:12.712433+00:00"},{"alias_kind":"pith_short_8","alias_value":"CDLELIOM","created_at":"2026-05-18T12:33:12.712433+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":5,"internal_anchor_count":3,"sample":[{"citing_arxiv_id":"2605.20866","citing_title":"LOSCAR-SGD: Local SGD with Communication-Computation Overlap and Delay-Corrected Sparse Model Averaging","ref_index":163,"is_internal_anchor":true},{"citing_arxiv_id":"2605.18174","citing_title":"Ringmaster LMO: Asynchronous Linear Minimization Oracle Momentum Method","ref_index":161,"is_internal_anchor":true},{"citing_arxiv_id":"2510.25372","citing_title":"Prompt Estimation from Prototypes for Federated Prompt Tuning of Vision Transformers","ref_index":8,"is_internal_anchor":true},{"citing_arxiv_id":"2605.08871","citing_title":"Rennala MVR: Improved Time Complexity for Parallel Stochastic Optimization via Momentum-Based Variance Reduction","ref_index":159,"is_internal_anchor":false},{"citing_arxiv_id":"2605.07795","citing_title":"Scalable Distributed Stochastic Optimization via Bidirectional Compression: Beyond Pessimistic Limits","ref_index":79,"is_internal_anchor":false}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/CDLELIOM2I6IRE56WJMEDMLFPV","json":"https://pith.science/pith/CDLELIOM2I6IRE56WJMEDMLFPV.json","graph_json":"https://pith.science/api/pith-number/CDLELIOM2I6IRE56WJMEDMLFPV/graph.json","events_json":"https://pith.science/api/pith-number/CDLELIOM2I6IRE56WJMEDMLFPV/events.json","paper":"https://pith.science/paper/CDLELIOM"},"agent_actions":{"view_html":"https://pith.science/pith/CDLELIOM2I6IRE56WJMEDMLFPV","download_json":"https://pith.science/pith/CDLELIOM2I6IRE56WJMEDMLFPV.json","view_paper":"https://pith.science/paper/CDLELIOM","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1906.04329&json=true","fetch_graph":"https://pith.science/api/pith-number/CDLELIOM2I6IRE56WJMEDMLFPV/graph.json","fetch_events":"https://pith.science/api/pith-number/CDLELIOM2I6IRE56WJMEDMLFPV/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/CDLELIOM2I6IRE56WJMEDMLFPV/action/timestamp_anchor","attest_storage":"https://pith.science/pith/CDLELIOM2I6IRE56WJMEDMLFPV/action/storage_attestation","attest_author":"https://pith.science/pith/CDLELIOM2I6IRE56WJMEDMLFPV/action/author_attestation","sign_citation":"https://pith.science/pith/CDLELIOM2I6IRE56WJMEDMLFPV/action/citation_signature","submit_replication":"https://pith.science/pith/CDLELIOM2I6IRE56WJMEDMLFPV/action/replication_record"}},"created_at":"2026-05-17T23:43:39.646590+00:00","updated_at":"2026-05-17T23:43:39.646590+00:00"}