ScionFL: Secure Quantized Aggregation for Federated Learning

@article{BenItzhak2022ScionFLSQ,
  title={ScionFL: Secure Quantized Aggregation for Federated Learning},
  author={Yaniv Ben-Itzhak and Helen Mollering and Benny Pinkas and T. Schneider and Ajith Suresh and Oleksandr Tkachenko and Shay Vargaftik and Christian Weinert and Hossein Yalame and Avishay Yanai},
  journal={ArXiv},
  year={2022},
  volume={abs/2210.07376}
}
Privacy concerns in federated learning (FL) are commonly addressed with secure aggregation schemes that prevent a central party from observing plaintext client updates. How-ever, most such schemes neglect orthogonal FL research that aims at reducing communication between clients and the aggregator and is instrumental in facilitating cross-device FL with thousands and even millions of (mobile) participants. In particular, quantization techniques can typically reduce client-server communication… 

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