Recoverable Identifier

arXiv:2605.00639 · detector doi_compliance · incontrovertible · 2026-05-19 17:57:34.279112+00:00

Paper page Integrity report arXiv Try DOI

Evidence text

Saha, S., Hota, A., Chattopadhyay, A. K., Nag, A. & Nandi, S. A Multifaceted Survey on Privacy Preservation of Federated Learning: Progress, Challenges, and Opportunities.Artificial Intelligence Review57,184. doi:10.1007/s10462-024-10766-7(June 2024). 13 Acknowledgments S.R.S. would like to thank Nicole Bryant and Harrison Dreves for their help organizing the ARROWS workshop and review. This work was authored in part by the National Laboratory of the Rockies (NLR) for the U.S. Department of Energy (DOE), operated under Contract No. DE-AC36-08GO28308. The views expressed in this article do not necessarily represent the views of the DOE or the U.S. Government. The U.S. Government retains and the publisher, by accepting the article for publication, acknowledges that the U.S. Government retains a nonexclusive, paid-up, irrevocable, worldwide license to publish or reproduce the published form of this work, or allow others to do so, for U.S. Government purposes. Funding Statement S.R.S., R.G., M.A.S., and A.Z. were supported as part of APEX (A Center for Power Electronics Materials and Manufacturing Exploration), an Energy Frontier Research Center funded by the U.S. Department of Energy, Office of Science, Basic Energy Sciences under Award #ERW0345 at NLR. R.C. gratefully acknowledges support from the National Science Foundation under DMR-2527684. Author Contributions S.R.S., H.E., and A.Z. led the writing and conceptualization. All the other co-authors participated in discussions 

Evidence payload

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  "reconstructed_doi": "10.1007/s10462-024-10766-7(June2024",
  "ref_index": 44,
  "resolved_title": null,
  "verdict_class": "incontrovertible"
}