{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,17]],"date-time":"2026-06-17T20:43:26Z","timestamp":1781729006788,"version":"3.54.5"},"reference-count":44,"publisher":"Wiley","issue":"6","license":[{"start":{"date-parts":[[2026,4,1]],"date-time":"2026-04-01T00:00:00Z","timestamp":1775001600000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/onlinelibrary.wiley.com\/termsAndConditions#vor"},{"start":{"date-parts":[[2026,4,1]],"date-time":"2026-04-01T00:00:00Z","timestamp":1775001600000},"content-version":"tdm","delay-in-days":0,"URL":"http:\/\/doi.wiley.com\/10.1002\/tdm_license_1.1"}],"content-domain":{"domain":["onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["Softw Pract Exp"],"published-print":{"date-parts":[[2026,6]]},"abstract":"<jats:title>ABSTRACT<\/jats:title>\n                  <jats:sec>\n                    <jats:title>Background<\/jats:title>\n                    <jats:p>Federated learning (FL) has emerged as a promising paradigm for distributed model training without centralizing raw data, yet it remains vulnerable to gradient leakage, inference attacks, and malicious updates. The integration of quantum technologies into FL, as proposed by Li et al. through two protocols for quantum federated learning (QFL), aims to mitigate these risks. However, a detailed cryptanalysis of their secure inner\u2010product estimation and incremental learning protocols reveals critical weaknesses, including susceptibility to replay attacks, quantum tomography, entanglement manipulation, and gradient inversion. These vulnerabilities become particularly severe in mission\u2010critical environments such as military communication networks, where compromised information may directly expose strategic operations and intelligence.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Objective<\/jats:title>\n                    <jats:p>To address these limitations, we propose MilQAuth, an improved quantum\u2010resistant authentication framework for QFL in military communication networks.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>The MilQAuth introduces a layered architecture that integrates lightweight authentication, privacy\u2010preserving encryption, and resilience against both classical and quantum adversaries. Formal validation is provided through the Real\u2010or\u2010Random (RoR) model and BAN logic, while automated security verification is conducted using Scyther.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>A comparative performance evaluation demonstrates that MilQAuth achieves reduced computational and communication costs while ensuring stronger privacy guarantees than existing QFL protocols.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusions<\/jats:title>\n                    <jats:p>The results confirm that the proposed solution, MilQAuth, not only resists known quantum\u2010era attacks but also satisfies the stringent security requirements of military\u2010grade communication systems. Beyond defense applications, the framework remains adaptable to other critical infrastructures, including healthcare and industrial IoT, where secure and privacy\u2010preserving federated learning is essential.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1002\/spe.70066","type":"journal-article","created":{"date-parts":[[2026,4,1]],"date-time":"2026-04-01T14:01:08Z","timestamp":1775052068000},"page":"709-733","update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["MilQAuth: An Improved and Quantum\u2010Resistant Authentication Framework for Quantum Federated Learning in Military Communication Networks"],"prefix":"10.1002","volume":"56","author":[{"given":"Zafar","family":"Iqbal","sequence":"first","affiliation":[{"name":"Department of Computer Science COMSATS University Islamabad, Sahiwal Campus  Sahiwal Pakistan"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6477-9445","authenticated-orcid":false,"given":"Syed Zohaib","family":"Hassan","sequence":"additional","affiliation":[{"name":"Department of Computer Science COMSATS University Islamabad, Sahiwal Campus  Sahiwal Pakistan"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0008-9350-3630","authenticated-orcid":false,"given":"Jie","family":"Zhao","sequence":"additional","affiliation":[{"name":"Department of Computer Science and Software Engineering Penn State University  Erie Pennsylvania USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Muhammad","family":"Javed","sequence":"additional","affiliation":[{"name":"Department of Computing and Information Technology, FOC Gomal University  D.I. Khan Pakistan"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1267-5510","authenticated-orcid":false,"given":"Mohammad Rafiq","family":"Mufti","sequence":"additional","affiliation":[{"name":"Department of Computer Science COMSATS University Islamabad, Vehari Campus  Vehari Pakistan"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Tahreem","family":"Saeed","sequence":"additional","affiliation":[{"name":"Department of Computer Science COMSATS University Islamabad, Sahiwal Campus  Sahiwal Pakistan"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Mohammad Shahid","family":"Zeb","sequence":"additional","affiliation":[{"name":"Govt Degree College No. 2  D.I. Khan Pakistan"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"311","published-online":{"date-parts":[[2026,4,1]]},"reference":[{"key":"e_1_2_16_2_1","doi-asserted-by":"publisher","DOI":"10.1088\/2058\u20109565\/ad40cc\/pdf"},{"key":"e_1_2_16_3_1","unstructured":"P.Guo R.Wang S.Zeng et al. \u201cA Deep Dive Into Gradient Inversion Attacks \u201d arXiv preprint arXiv:2503.11514 (2025) https:\/\/arxiv.org\/abs\/2503.11514."},{"key":"e_1_2_16_4_1","volume-title":"Advances in Neural Information Processing Systems (NeurIPS)","author":"Huang Y.","year":"2021"},{"key":"e_1_2_16_5_1","first-page":"1","volume-title":"Proceedings of Machine Learning Research","author":"Wu R.","year":"2023"},{"key":"e_1_2_16_6_1","doi-asserted-by":"crossref","unstructured":"J.Xu C.Hong J.Huang L. Y.Chen andJ.Decouchant \u201cAGIC: Approximate Gradient Inversion Attack on Federated Learning \u201d arXiv preprint arXiv:2204.13784 (2022) https:\/\/arxiv.org\/abs\/2204.13784.","DOI":"10.1109\/SRDS55811.2022.00012"},{"key":"e_1_2_16_7_1","doi-asserted-by":"publisher","DOI":"10.1137\/S0097539795293172"},{"key":"e_1_2_16_8_1","doi-asserted-by":"publisher","DOI":"10.1145\/237814.237866"},{"key":"e_1_2_16_9_1","article-title":"PQSF: Post\u2010Quantum Secure Federated Learning","volume":"14","author":"Zhang X.","year":"2024","journal-title":"Scientific Reports"},{"key":"e_1_2_16_10_1","doi-asserted-by":"crossref","unstructured":"S.AhmedandM. H.Anisi \u201cA Post\u2010Quantum Secure Federated Learning Framework for Cross Domain V2G Authentication \u201d2025 https:\/\/repository.essex.ac.uk\/41110\/1\/A_Post_Quantum_Secure_Federated_Learning_Framework_for_Cross_Domain_V2G_Authentication.pdf.","DOI":"10.1109\/TCE.2025.3580338"},{"key":"e_1_2_16_11_1","unstructured":"S.Gurung P. K.Pokhrel andF.Li \u201cSecure Communication Model for Quantum Federated Learning: A PQC Framework \u201d arXiv preprint arXiv:2304.13413 (2023) https:\/\/arxiv.org\/abs\/2304.13413."},{"key":"e_1_2_16_12_1","doi-asserted-by":"publisher","DOI":"10.1007\/s44443\u2010025\u201000029\u2010y"},{"key":"e_1_2_16_13_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.knosys.2024.112115"},{"key":"e_1_2_16_14_1","doi-asserted-by":"crossref","unstructured":"D.CommeyandG. V.Crosby \u201cPQS\u2010BFL: A Post\u2010Quantum Secure Blockchain\u2010Based Federated Learning Framework \u201d arXiv preprint arXiv:2505.01866 (2025) https:\/\/arxiv.org\/abs\/2505.01866.","DOI":"10.1016\/j.eswa.2026.131449"},{"key":"e_1_2_16_15_1","doi-asserted-by":"crossref","unstructured":"H.Gharavi J.Granjai andE.Monteiro \u201cPQBFL: A Post\u2010Quantum Blockchain\u2010Based Protocol for Federated Learning \u201d arXiv preprint arXiv:2502.14464 (2025) https:\/\/arxiv.org\/abs\/2502.14464.","DOI":"10.2139\/ssrn.5191890"},{"key":"e_1_2_16_16_1","article-title":"Shadow Defense Against Gradient Inversion Attack","volume":"93","author":"Jiang L.","year":"2025","journal-title":"Medical Image Analysis"},{"key":"e_1_2_16_17_1","doi-asserted-by":"publisher","DOI":"10.1109\/TBDATA.2023.3239116"},{"key":"e_1_2_16_18_1","unstructured":"P.Guo S.Zeng W.Chen et al. \u201cHyperFL: A New Federated Learning Framework Against Gradient Inversion Attacks \u201d arXiv preprint arXiv:2412.07187 (2024) https:\/\/arxiv.org\/html\/2412.07187v1."},{"key":"e_1_2_16_19_1","doi-asserted-by":"publisher","DOI":"10.1007\/3-540-48329-2_21"},{"key":"e_1_2_16_20_1","doi-asserted-by":"publisher","DOI":"10.1145\/77648.77649"},{"key":"e_1_2_16_21_1","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-319-10082-1_3"},{"key":"e_1_2_16_22_1","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-540-70545-1_38"},{"key":"e_1_2_16_23_1","unstructured":"N.Kumar J.Heredge C.Li et al. \u201cExpressive Variational Quantum Circuits Provide Inherent Privacy in Federated Learning \u201d arXiv preprint arXiv:2309.13002 (2023) https:\/\/arxiv.org\/abs\/2309.13002."},{"key":"e_1_2_16_24_1","unstructured":"Z.Wang N.Dong J.Sun W.Knottenbelt andY.Guo \u201czkFL: Zero\u2010Knowledge Proof\u2010Based Gradient Aggregation for Federated Learning \u201d arXiv preprint arXiv:2310.02554 (2023) https:\/\/arxiv.org\/pdf\/2310.02554."},{"key":"e_1_2_16_25_1","unstructured":"D. C.Nguyen M. R.Uddin S.Shaon R.Rahman O.Dobre andD.Niyato \u201cQuantum Federated Learning: A Comprehensive Survey \u201d arXiv preprint arXiv:2508.15998 (2025) https:\/\/arxiv.org\/html\/2508.15998v1."},{"key":"e_1_2_16_26_1","first-page":"653","article-title":"Securing Wireless Sensors in Military Applications","volume":"170","author":"Jain U.","year":"2020","journal-title":"Procedia Computer Science"},{"key":"e_1_2_16_27_1","unstructured":"A.Ray \u201cEdgeAgentX: A Novel Framework for Agentic AI at the Edge in Military Communication Networks \u201d arXiv preprint arXiv:2505.18457 (2025) https:\/\/arxiv.org\/abs\/2505.18457:contentReference[oaicite:1]index=1."},{"key":"e_1_2_16_28_1","doi-asserted-by":"crossref","unstructured":"Y.Lee T.Park Y.Lee J.Gong andJ.Kang \u201cExploring Potential Prompt Injection Attacks in Federated Military LLMs and Their Mitigation \u201d arXiv preprint arXiv:2501.18416 (2025) https:\/\/arxiv.org\/abs\/2501.18416.","DOI":"10.1109\/BigData66926.2025.11401933"},{"key":"e_1_2_16_29_1","doi-asserted-by":"crossref","unstructured":"J.Qian K.Wei Y.Wu J.Zhang J.Chen andH.Bao \u201cGI\u2010SMN: Gradient Inversion Attack Against Federated Learning Without Prior Knowledge \u201d arXiv preprint arXiv:2405.03516 (2024) https:\/\/arxiv.org\/abs\/2405.03516.","DOI":"10.1007\/978-981-97-5603-2_36"},{"key":"e_1_2_16_30_1","unstructured":"V.Valadi M.\u00c5kesson J.\u00d8stman S.Toor andA.Hellander \u201cFrom Research to Reality: Feasibility of Gradient Inversion Attacks in Federated Learning \u201d arXiv preprint arXiv:2508.19819 (2025) https:\/\/arxiv.org\/abs\/2508.19819."},{"key":"e_1_2_16_31_1","unstructured":"D.Scheliga P.M\u00e4der andM.Seeland \u201cCombining Stochastic Defenses to Resist Gradient Inversion: An Ablation Study \u201d arXiv preprint arXiv:2208.04767 (2022) https:\/\/arxiv.org\/abs\/2208.04767."},{"key":"e_1_2_16_32_1","article-title":"E2EGI: End\u2010To\u2010End Gradient Inversion in Federated Learning","volume":"82","author":"Li Z.","year":"2023","journal-title":"Medical Image Analysis"},{"key":"e_1_2_16_33_1","volume-title":"Proceedings of the 34th USENIX Security Symposium","author":"Carletti V.","year":"2025"},{"key":"e_1_2_16_34_1","doi-asserted-by":"publisher","DOI":"10.1103\/PhysRevLett.91.057901"},{"key":"e_1_2_16_35_1","doi-asserted-by":"publisher","DOI":"10.1103\/PhysRevLett.94.230504"},{"key":"e_1_2_16_36_1","doi-asserted-by":"publisher","DOI":"10.24963\/ijcai.2022\/791"},{"key":"e_1_2_16_37_1","doi-asserted-by":"publisher","DOI":"10.1007\/s10462\u2010025\u201011248\u20100"},{"key":"e_1_2_16_38_1","doi-asserted-by":"publisher","DOI":"10.1007\/s11276\u2010024\u201003667\u20108"},{"key":"e_1_2_16_39_1","doi-asserted-by":"publisher","DOI":"10.1038\/s41598-025-14341-0"},{"key":"e_1_2_16_40_1","unstructured":"D.Hitaj G.Pagnotta B.Hitaj F.Perez andL. V.Mancini \u201cFedComm: Federated Learning as a Medium for Covert Communication \u201d arXiv preprint arXiv:2201.08786 (2022) https:\/\/arxiv.org\/abs\/2201.08786."},{"key":"e_1_2_16_41_1","unstructured":"J.LiangandR.Wang \u201cCovert Communication Based on the Poisoning Attack in Federated Learning \u201d arXiv preprint arXiv:2306.01342 (2023) https:\/\/arxiv.org\/abs\/2306.01342."},{"key":"e_1_2_16_42_1","unstructured":"wolfSSL \u201cPost\u2010Quantum Cryptography Benchmarks on Cortex\u2010M4 (PQM4) \u201d2023 https:\/\/www.wolfssl.com\/post\u2010quantum\u2010cryptography\u2010benchmarks\u2010pqm4\/."},{"key":"e_1_2_16_43_1","unstructured":"NIST \u201cNIST Post\u2010Quantum Cryptography Project: Performance Benchmarks \u201d2023 https:\/\/csrc.nist.gov\/projects\/post\u2010quantum\u2010cryptography\/performance."},{"key":"e_1_2_16_44_1","doi-asserted-by":"publisher","DOI":"10.1109\/TII.2022.3193478"},{"key":"e_1_2_16_45_1","doi-asserted-by":"publisher","DOI":"10.1109\/TIT.1983.1056650"}],"container-title":["Software: Practice and Experience"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/onlinelibrary.wiley.com\/doi\/pdf\/10.1002\/spe.70066","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/onlinelibrary.wiley.com\/doi\/full-xml\/10.1002\/spe.70066","content-type":"application\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/onlinelibrary.wiley.com\/doi\/pdf\/10.1002\/spe.70066","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,5,16]],"date-time":"2026-05-16T03:08:34Z","timestamp":1778900914000},"score":1,"resource":{"primary":{"URL":"https:\/\/onlinelibrary.wiley.com\/doi\/10.1002\/spe.70066"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,4,1]]},"references-count":44,"journal-issue":{"issue":"6","published-print":{"date-parts":[[2026,6]]}},"alternative-id":["10.1002\/spe.70066"],"URL":"https:\/\/doi.org\/10.1002\/spe.70066","archive":["Portico"],"relation":{},"ISSN":["0038-0644","1097-024X"],"issn-type":[{"value":"0038-0644","type":"print"},{"value":"1097-024X","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,4,1]]},"assertion":[{"value":"2025-11-18","order":0,"name":"received","label":"Received","group":{"name":"publication_history","label":"Publication History"}},{"value":"2026-03-17","order":2,"name":"accepted","label":"Accepted","group":{"name":"publication_history","label":"Publication History"}},{"value":"2026-04-01","order":3,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}