{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2022:6IXZZGRZEODXYEKQMJUZJ52MDU","short_pith_number":"pith:6IXZZGRZ","schema_version":"1.0","canonical_sha256":"f22f9c9a3923877c1150626994f74c1d1181193b45e9d570e95e335983008492","source":{"kind":"arxiv","id":"2209.03839","version":2},"attestation_state":"computed","paper":{"title":"FADE: Enabling Federated Adversarial Training on Heterogeneous Resource-Constrained Edge Devices","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.LG","authors_text":"Amin Hassanzadeh, Aolin Ding, Hai Li, Jianyi Zhang, Louis DiValentin, Mingyuan Ma, Minxue Tang, Yiran Chen","submitted_at":"2022-09-08T14:22:49Z","abstract_excerpt":"Federated adversarial training can effectively complement adversarial robustness into the privacy-preserving federated learning systems. However, the high demand for memory capacity and computing power makes large-scale federated adversarial training infeasible on resource-constrained edge devices. Few previous studies in federated adversarial training have tried to tackle both memory and computational constraints simultaneously. In this paper, we propose a new framework named Federated Adversarial Decoupled Learning (FADE) to enable AT on heterogeneous resource-constrained edge devices. FADE "},"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":"2209.03839","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2022-09-08T14:22:49Z","cross_cats_sorted":[],"title_canon_sha256":"db6f5618e8cb5533a132d21f60d4993b74bb3746f79b49f620a61d1e91226767","abstract_canon_sha256":"ec9f635823e523ffb7fbf0ed2c693dd8cbd772dcd16bff08750cb68f8c3a17c6"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T06:04:34.602168Z","signature_b64":"TWJ4IEvMqih6BIbnrxe7ygaM24lPPE6AKXRi/dBqirzNCOPc/hObYOnmzKzAPYdF+UbvkqKGzMopM4+HiXvzAw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"f22f9c9a3923877c1150626994f74c1d1181193b45e9d570e95e335983008492","last_reissued_at":"2026-07-05T06:04:34.601797Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T06:04:34.601797Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"FADE: Enabling Federated Adversarial Training on Heterogeneous Resource-Constrained Edge Devices","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.LG","authors_text":"Amin Hassanzadeh, Aolin Ding, Hai Li, Jianyi Zhang, Louis DiValentin, Mingyuan Ma, Minxue Tang, Yiran Chen","submitted_at":"2022-09-08T14:22:49Z","abstract_excerpt":"Federated adversarial training can effectively complement adversarial robustness into the privacy-preserving federated learning systems. However, the high demand for memory capacity and computing power makes large-scale federated adversarial training infeasible on resource-constrained edge devices. Few previous studies in federated adversarial training have tried to tackle both memory and computational constraints simultaneously. In this paper, we propose a new framework named Federated Adversarial Decoupled Learning (FADE) to enable AT on heterogeneous resource-constrained edge devices. FADE "},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2209.03839","kind":"arxiv","version":2},"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/2209.03839/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"},"aliases":[{"alias_kind":"arxiv","alias_value":"2209.03839","created_at":"2026-07-05T06:04:34.601849+00:00"},{"alias_kind":"arxiv_version","alias_value":"2209.03839v2","created_at":"2026-07-05T06:04:34.601849+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2209.03839","created_at":"2026-07-05T06:04:34.601849+00:00"},{"alias_kind":"pith_short_12","alias_value":"6IXZZGRZEODX","created_at":"2026-07-05T06:04:34.601849+00:00"},{"alias_kind":"pith_short_16","alias_value":"6IXZZGRZEODXYEKQ","created_at":"2026-07-05T06:04:34.601849+00:00"},{"alias_kind":"pith_short_8","alias_value":"6IXZZGRZ","created_at":"2026-07-05T06:04:34.601849+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":0,"internal_anchor_count":0,"sample":[]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/6IXZZGRZEODXYEKQMJUZJ52MDU","json":"https://pith.science/pith/6IXZZGRZEODXYEKQMJUZJ52MDU.json","graph_json":"https://pith.science/api/pith-number/6IXZZGRZEODXYEKQMJUZJ52MDU/graph.json","events_json":"https://pith.science/api/pith-number/6IXZZGRZEODXYEKQMJUZJ52MDU/events.json","paper":"https://pith.science/paper/6IXZZGRZ"},"agent_actions":{"view_html":"https://pith.science/pith/6IXZZGRZEODXYEKQMJUZJ52MDU","download_json":"https://pith.science/pith/6IXZZGRZEODXYEKQMJUZJ52MDU.json","view_paper":"https://pith.science/paper/6IXZZGRZ","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2209.03839&json=true","fetch_graph":"https://pith.science/api/pith-number/6IXZZGRZEODXYEKQMJUZJ52MDU/graph.json","fetch_events":"https://pith.science/api/pith-number/6IXZZGRZEODXYEKQMJUZJ52MDU/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/6IXZZGRZEODXYEKQMJUZJ52MDU/action/timestamp_anchor","attest_storage":"https://pith.science/pith/6IXZZGRZEODXYEKQMJUZJ52MDU/action/storage_attestation","attest_author":"https://pith.science/pith/6IXZZGRZEODXYEKQMJUZJ52MDU/action/author_attestation","sign_citation":"https://pith.science/pith/6IXZZGRZEODXYEKQMJUZJ52MDU/action/citation_signature","submit_replication":"https://pith.science/pith/6IXZZGRZEODXYEKQMJUZJ52MDU/action/replication_record"}},"created_at":"2026-07-05T06:04:34.601849+00:00","updated_at":"2026-07-05T06:04:34.601849+00:00"}