{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2021:O5LZ7BHA5QF64ZYJV7T3NJW25P","short_pith_number":"pith:O5LZ7BHA","canonical_record":{"source":{"id":"2105.14772","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2021-05-31T08:15:44Z","cross_cats_sorted":["cs.AI","cs.DC"],"title_canon_sha256":"c46d61c4c664628a81c4034bd4a5e76d506efc6a412b547db63d89d820b3f7dc","abstract_canon_sha256":"e1e863d1c2dee704deed9a18bb5a7de3f47fa9c38e9318928c90fd8870176f28"},"schema_version":"1.0"},"canonical_sha256":"77579f84e0ec0bee6709afe7b6a6daebf4f843d66ab14a0edfaaa0075b023084","source":{"kind":"arxiv","id":"2105.14772","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2105.14772","created_at":"2026-07-05T02:44:41Z"},{"alias_kind":"arxiv_version","alias_value":"2105.14772v1","created_at":"2026-07-05T02:44:41Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2105.14772","created_at":"2026-07-05T02:44:41Z"},{"alias_kind":"pith_short_12","alias_value":"O5LZ7BHA5QF6","created_at":"2026-07-05T02:44:41Z"},{"alias_kind":"pith_short_16","alias_value":"O5LZ7BHA5QF64ZYJ","created_at":"2026-07-05T02:44:41Z"},{"alias_kind":"pith_short_8","alias_value":"O5LZ7BHA","created_at":"2026-07-05T02:44:41Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2021:O5LZ7BHA5QF64ZYJV7T3NJW25P","target":"record","payload":{"canonical_record":{"source":{"id":"2105.14772","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2021-05-31T08:15:44Z","cross_cats_sorted":["cs.AI","cs.DC"],"title_canon_sha256":"c46d61c4c664628a81c4034bd4a5e76d506efc6a412b547db63d89d820b3f7dc","abstract_canon_sha256":"e1e863d1c2dee704deed9a18bb5a7de3f47fa9c38e9318928c90fd8870176f28"},"schema_version":"1.0"},"canonical_sha256":"77579f84e0ec0bee6709afe7b6a6daebf4f843d66ab14a0edfaaa0075b023084","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T02:44:41.161081Z","signature_b64":"aECFYykrJAigAdhAgygfFil7miFOt+BAkX8sHLIbwP/dFeCT8DID47zxXSPFzuY2C/puWtdnL/1o1MXMvkAUAw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"77579f84e0ec0bee6709afe7b6a6daebf4f843d66ab14a0edfaaa0075b023084","last_reissued_at":"2026-07-05T02:44:41.160658Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T02:44:41.160658Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2105.14772","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":"2026-07-05T02:44:41Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"YUGp1mCTSL+ItkP209rgDYIx07JxNVxeqDFcix9EIn5BBIY65kK+fuLSOaUgegl6tsi+789yV5iPInzCbf83AQ==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-06T06:52:22.850440Z"},"content_sha256":"1ba1196d2b3c0ee3357aa34bcc3095922f240b3e9c935e395dd90c5b474991cd","schema_version":"1.0","event_id":"sha256:1ba1196d2b3c0ee3357aa34bcc3095922f240b3e9c935e395dd90c5b474991cd"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2021:O5LZ7BHA5QF64ZYJV7T3NJW25P","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Energy-Efficient and Federated Meta-Learning via Projected Stochastic Gradient Ascent","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.AI","cs.DC"],"primary_cat":"cs.LG","authors_text":"Amrit S. Bedi, Anis Elgabli, Chaouki Ben Issaid, Mehdi Bennis, Vaneet Aggarwal","submitted_at":"2021-05-31T08:15:44Z","abstract_excerpt":"In this paper, we propose an energy-efficient federated meta-learning framework. The objective is to enable learning a meta-model that can be fine-tuned to a new task with a few number of samples in a distributed setting and at low computation and communication energy consumption. We assume that each task is owned by a separate agent, so a limited number of tasks is used to train a meta-model. Assuming each task was trained offline on the agent's local data, we propose a lightweight algorithm that starts from the local models of all agents, and in a backward manner using projected stochastic g"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2105.14772","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/2105.14772/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":"2026-07-05T02:44:41Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"jtpyVXmtRAvwNfNESFgcK0R20qb1rBP7LfuqNCc1Q61KYAbqCX7Qwn59nGNvlLdPsks8ytL8qVptMZOR4YvbDw==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-06T06:52:22.851385Z"},"content_sha256":"00e09b28750889b3e5ecfb5e6a30f6fddf5bfcebde6fdd793d567624314b4c2f","schema_version":"1.0","event_id":"sha256:00e09b28750889b3e5ecfb5e6a30f6fddf5bfcebde6fdd793d567624314b4c2f"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/O5LZ7BHA5QF64ZYJV7T3NJW25P/bundle.json","state_url":"https://pith.science/pith/O5LZ7BHA5QF64ZYJV7T3NJW25P/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/O5LZ7BHA5QF64ZYJV7T3NJW25P/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":"2026-08-06T06:52:22Z","links":{"resolver":"https://pith.science/pith/O5LZ7BHA5QF64ZYJV7T3NJW25P","bundle":"https://pith.science/pith/O5LZ7BHA5QF64ZYJV7T3NJW25P/bundle.json","state":"https://pith.science/pith/O5LZ7BHA5QF64ZYJV7T3NJW25P/state.json","well_known_bundle":"https://pith.science/.well-known/pith/O5LZ7BHA5QF64ZYJV7T3NJW25P/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2021:O5LZ7BHA5QF64ZYJV7T3NJW25P","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":"e1e863d1c2dee704deed9a18bb5a7de3f47fa9c38e9318928c90fd8870176f28","cross_cats_sorted":["cs.AI","cs.DC"],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2021-05-31T08:15:44Z","title_canon_sha256":"c46d61c4c664628a81c4034bd4a5e76d506efc6a412b547db63d89d820b3f7dc"},"schema_version":"1.0","source":{"id":"2105.14772","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2105.14772","created_at":"2026-07-05T02:44:41Z"},{"alias_kind":"arxiv_version","alias_value":"2105.14772v1","created_at":"2026-07-05T02:44:41Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2105.14772","created_at":"2026-07-05T02:44:41Z"},{"alias_kind":"pith_short_12","alias_value":"O5LZ7BHA5QF6","created_at":"2026-07-05T02:44:41Z"},{"alias_kind":"pith_short_16","alias_value":"O5LZ7BHA5QF64ZYJ","created_at":"2026-07-05T02:44:41Z"},{"alias_kind":"pith_short_8","alias_value":"O5LZ7BHA","created_at":"2026-07-05T02:44:41Z"}],"graph_snapshots":[{"event_id":"sha256:00e09b28750889b3e5ecfb5e6a30f6fddf5bfcebde6fdd793d567624314b4c2f","target":"graph","created_at":"2026-07-05T02:44:41Z","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/2105.14772/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"In this paper, we propose an energy-efficient federated meta-learning framework. The objective is to enable learning a meta-model that can be fine-tuned to a new task with a few number of samples in a distributed setting and at low computation and communication energy consumption. We assume that each task is owned by a separate agent, so a limited number of tasks is used to train a meta-model. Assuming each task was trained offline on the agent's local data, we propose a lightweight algorithm that starts from the local models of all agents, and in a backward manner using projected stochastic g","authors_text":"Amrit S. Bedi, Anis Elgabli, Chaouki Ben Issaid, Mehdi Bennis, Vaneet Aggarwal","cross_cats":["cs.AI","cs.DC"],"headline":"","license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2021-05-31T08:15:44Z","title":"Energy-Efficient and Federated Meta-Learning via Projected Stochastic Gradient Ascent"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2105.14772","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":"sha256:1ba1196d2b3c0ee3357aa34bcc3095922f240b3e9c935e395dd90c5b474991cd","target":"record","created_at":"2026-07-05T02:44:41Z","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":"e1e863d1c2dee704deed9a18bb5a7de3f47fa9c38e9318928c90fd8870176f28","cross_cats_sorted":["cs.AI","cs.DC"],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2021-05-31T08:15:44Z","title_canon_sha256":"c46d61c4c664628a81c4034bd4a5e76d506efc6a412b547db63d89d820b3f7dc"},"schema_version":"1.0","source":{"id":"2105.14772","kind":"arxiv","version":1}},"canonical_sha256":"77579f84e0ec0bee6709afe7b6a6daebf4f843d66ab14a0edfaaa0075b023084","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"77579f84e0ec0bee6709afe7b6a6daebf4f843d66ab14a0edfaaa0075b023084","first_computed_at":"2026-07-05T02:44:41.160658Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T02:44:41.160658Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"aECFYykrJAigAdhAgygfFil7miFOt+BAkX8sHLIbwP/dFeCT8DID47zxXSPFzuY2C/puWtdnL/1o1MXMvkAUAw==","signature_status":"signed_v1","signed_at":"2026-07-05T02:44:41.161081Z","signed_message":"canonical_sha256_bytes"},"source_id":"2105.14772","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:1ba1196d2b3c0ee3357aa34bcc3095922f240b3e9c935e395dd90c5b474991cd","sha256:00e09b28750889b3e5ecfb5e6a30f6fddf5bfcebde6fdd793d567624314b4c2f"],"state_sha256":"dfb6c594e71767a4ea1b9c1c7836a62c17a9464fc496d02e8cb1ec8a3fd2c72a"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"oxSK5j3ZuoymQPQgvBpX6UtWOr1zPXyzbctlUyEcTKfLqjMuMVYioS/sSO2P2KFTON5F8wYqMn26Wklpf+wHDw==","signed_message":"bundle_sha256_bytes","signed_at":"2026-08-06T06:52:22.858923Z","bundle_sha256":"54232f9b95b69895118a539f16d7c0d4939378890d70fbfb5f3df0d303a552fe"}}