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However, under extremely non-IID settings, existing data-free methods often generate low-quality data that suffers from severe semantic misalignment with ground-truth labels. To overcome these issues, we propose a novel Federated Model Inversion and Token Relabel (FedMITR) framework, which trains the global model by fully exploiting all patches of synthetic images. Specifically, FedMITR employs sparse model inversion during data generation, selectively inverting semantic foregrounds while halting the inversion of uninformative backgrounds. To address semantically meaningless tokens that hinder ViT predictions, we implement a differentiated strategy: patches with high information density utilize generated pseudo-labels, while patches with low information density are relabeled via ensemble models","title":"Provable Sparse Inversion and Token Relabel Enhanced One-shot Federated Learning with ViTs","url":"https://arxiv.org/abs/2605.10748","vendor":"arxiv_cs_ai"},"summary":"arXiv:2605.10748v1 Announce Type: cross \nAbstract: One-Shot Federated Learning, where a central server learns a global model in a single communication round, has emerged as a promising paradigm. However, under extremely non-IID settings, existing data-free methods often generate low-quality data that suffers from severe semantic misalignment with ground-truth labels. To overcome these issues, we propose a novel Federated Model Inversion and Token Relabel (FedMITR) framework, which trains the global model by fully exploiting all patches of synthetic images. Specifically, FedMITR employs sparse model inversion during data generation, selectively inverting semantic foregrounds while halting the inversion of uninformative backgrounds. To address semantically meaningless tokens that hinder ViT predictions, we implement a differentiated strategy: patches with high information density utilize generated pseudo-labels, while patches with low information density are relabeled via ensemble models","title":"Provable Sparse Inversion and Token Relabel Enhanced One-shot Federated Learning with ViTs","vendor":"arxiv_cs_ai"},"confidence":{"method":"deterministic"},"decision":"POSITIVE","issued_at":"2026-05-12T04:43:42Z","notes":"Spider vendor_press (news) news.vendor_press.v1","object":{"captured_by":"crovia.spider.vendor_press","primary_source_url":"https://arxiv.org/abs/2605.10748"},"predecessors":[],"schema":"crovia.axiom.v1","signature":"ed25519:fbd79b8dea97e746ef7b5fc8ba45ac1ba1dd35e821ed4f5c2c2bd05b61d47d5cc8ac1a1323fa67e5003bc7b7fb5fa07a0439781e8f7f0364bf24ae4e02496603","signer":"crovia.substrate","subject":{"observed_at":"2026-05-12T04:43:42Z","source_collector":"spider:vendor_press","target_id":"https://arxiv.org/abs/2605.10748"},"tsa":{"authority":"crovia.substrate.bootstrap","rfc3161_token":"{\"kind\":\"crovia.bootstrap.tsa\",\"source_jsonl\":\"/opt/crovia/spider/data/news/vendor_press_v1.jsonl\",\"source_seal_merkle_root\":\"spider_vendor_press_v1\",\"upgrade_path\":\"Sessione H \\u2014 OpenTimestamps weekly anchor\"}"},"zk_mode":"clear","zk_proof":null},"ledger":{"leaf_hash":"2665d866ce861e8834e3e16a3684cc09cad6492ca1ae6cb937f118dacb226dfa","leaf_index":128911,"ledger_path":"/opt/crovia/substrate/axiom_ledger.jsonl"},"merkle_proof":{"hash_alg":"sha256","leaf_prefix":"0x00","node_prefix":"0x01","odd_leaf_rule":"duplicate_last","path":[{"sibling":"262bd6884e993c107797012d4ed61140205422e08514dbf9c1081ffaad8843ff","side":"left"},{"sibling":"af394ecbc4298e1d67dea57420acd94a0235731cf10c8d8d140d42b079cf4a26","side":"left"},{"sibling":"7d4478680cf6ad03aaceb2776baf2c06a627a870e7a4186fe9c78711d381bb4b","side":"left"},{"sibling":"ec6ea0e3e66011889283cf9b8cd0b49e2de4c74f1b4cef7e26629bffff400745","side":"left"},{"sibling":"cf04a3bd73711890b4008d1e98c917463e44cf575400008b82fc966134b45143","side":"right"},{"sibling":"bca4ae7c2ec561b665545a6f2f3e419a53afed4a27b8209574392a7c1cbc9439","side":"right"},{"sibling":"c76bba0c216c73590d44e9b2dbdda205cb4803404f532bfebb832fe2ca20fafe","side":"right"},{"sibling":"5eb042446399e2602ae7dd8d9f9f2cac58abf22ff48527ae02c589594da10510","side":"left"},{"sibling":"9b7895252355a8c8a373671590682bbf4d36d41befce49b5e889eaf896415156","side":"left"},{"sibling":"1d74b0fb79eace68949b4d82b0e430b1d9eb122c125f67f5be7d50c074c228e7","side":"left"},{"sibling":"c6eaf7a4fcab2db96e9e9423acb6922c80f64882d0f3f50d09e53a4807d23084","side":"left"},{"sibling":"df12eaabc0a370aff5d0488478f48d2b3f90d025c903643f98ea804b017688ec","side":"right"},{"sibling":"ffc4d51379293bc3e1910c7d612f409dc610fd9acf8241793fb89f82e1bad4ef","side":"left"},{"sibling":"62ac6554017807bd83187f5a3e5f4f72d6c482616429c2780e9fff1f4845fa04","side":"left"},{"sibling":"3a5e69cf0803f4c91f3895ed7c9a95748fef240bec4422e167c05300f79f06c0","side":"left"},{"sibling":"f2817ab288b5324fe49770372c7a10f33f7cd11005f8d4c0a730316f5229dc98","side":"left"},{"sibling":"725fac972e772ca0dc598810ea1abc70df472f72d2d6ab8a0baee2b80e5d2f4c","side":"left"},{"sibling":"98fc57dfef8873b512edc8340f7181df57302bb96777625e072235c62d7c5895","side":"right"}]},"schema":"crovia.axiom_proof.v1","seal":{"first_collector_run_id":"","first_receipt_hash":"","jsonl_path":"/opt/crovia/substrate/axiom_ledger.jsonl","key_id":"430895f101d38164","last_collector_run_id":"","last_receipt_hash":"","leaf_count":134292,"merkle_root":"77fc9c28fae777b81da5b495b3115474df6592dfac590333213d3bdf8b94a9b3","public_key_hex":"cf742e26f75669dc673cb5c0786a1ae23ae8ca19c347317192ce40c28a7ff25c","run_id":"hourly_json_retrofit_20260515T023701Z","schema":"crovia.seal.v1","seal_family_version":"crovia-seal-family/1","seal_kind":"substrate_batch","sealed_at":"2026-05-15T02:37:25Z","sig_algorithm":"ed25519","signature":"68107a834b00b24f5d4501e5ec727445311f132a486567ecc4c72a4e6dff24c8c21f2de3105293353ba5fdbe370d032819af6aa70f694e2e39b6af6737507009","signer_version":"1.1.0"},"trust_root":{"key_id":"430895f101d38164","public_key_hex":"cf742e26f75669dc673cb5c0786a1ae23ae8ca19c347317192ce40c28a7ff25c","signature_algorithm":"ed25519","url":"/registry/canon/TRUST_ROOT.md"},"verifier":{"spec":"/registry/canon/AXIOM_RECEIPT_v1.md","url":"/v/axm_a73cbe8c9a68c315c1aa3e82521c3055ccbbab96204d2f09e88cfdcdfb24c438"}}