论文标题
编码联合学习中基于计算代码的隐私
Computational Code-Based Privacy in Coded Federated Learning
论文作者
论文摘要
我们提出了一个保存隐私的联合学习（FL）计划，该计划对散落设备有弹性。建议一种自适应方案，较慢的设备与更快的设备共享其数据，并且不参与学习过程。所提出的方案采用基于代码的密码学来确保私人数据的\ emph {Computational}隐私，即，没有具有有界计算功率的设备可以在可行的时间内获取有关其他设备数据的信息。对于具有25个设备的情况，与常规的迷你批量FL相比，MNIST数据集的精度为95 \％，分别达到92和128位安全性的4.7和4的加速度为95 \％。
We propose a privacy-preserving federated learning (FL) scheme that is resilient against straggling devices. An adaptive scenario is suggested where the slower devices share their data with the faster ones and do not participate in the learning process. The proposed scheme employs code-based cryptography to ensure \emph{computational} privacy of the private data, i.e., no device with bounded computational power can obtain information about the other devices' data in feasible time. For a scenario with 25 devices, the proposed scheme achieves a speed-up of 4.7 and 4 for 92 and 128 bits security, respectively, for an accuracy of 95\% on the MNIST dataset compared with conventional mini-batch FL.
