FedML-HE: An Efficient Homomorphic-Encryption-Based Privacy-Preserving Federated Learning System. Jin, W., Yao, Y., Han, S., Joe-Wong, C., Ravi, S., Avestimehr, S., & He, C. CoRR, 2023.
FedML-HE: An Efficient Homomorphic-Encryption-Based Privacy-Preserving Federated Learning System [link]Paper  doi  bibtex   
@article{DBLP:journals/corr/abs-2303-10837,
  author       = {Weizhao Jin and
                  Yuhang Yao and
                  Shanshan Han and
                  Carlee Joe{-}Wong and
                  Srivatsan Ravi and
                  Salman Avestimehr and
                  Chaoyang He},
  title        = {FedML-HE: An Efficient Homomorphic-Encryption-Based Privacy-Preserving
                  Federated Learning System},
  journal      = {CoRR},
  volume       = {abs/2303.10837},
  year         = {2023},
  url          = {https://doi.org/10.48550/arXiv.2303.10837},
  doi          = {10.48550/ARXIV.2303.10837},
  eprinttype    = {arXiv},
  eprint       = {2303.10837},
  timestamp    = {Wed, 22 Mar 2023 14:41:36 +0100},
  biburl       = {https://dblp.org/rec/journals/corr/abs-2303-10837.bib},
  bibsource    = {dblp computer science bibliography, https://dblp.org}
}

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