text: 482,36,1364295810717011974,373525906,Weijie Su,"New paper: In *Federated f-Differential Privacy* (<LINK>), we proposed a new privacy notion tailored to the setting where the clients ally in the attack. This privacy concept is adapted from f-differential privacy. w/ Qinqing Zheng, @ShuxiaoC, and Qi Long.",https://arxiv.org/abs/2102.11158,"Federated learning (FL) is a training paradigm where the clients collaboratively learn models by repeatedly sharing information without compromising much on the privacy of their local sensitive data. In this paper, we introduce federated $f$-differential privacy, a new notion specifically tailored to the federated setting, based on the framework of Gaussian differential privacy. Federated $f$-differential privacy operates on record level: it provides the privacy guarantee on each individual record of one client's data against adversaries. We then propose a generic private federated learning framework {PriFedSync} that accommodates a large family of state-of-the-art FL algorithms, which provably achieves federated $f$-differential privacy. Finally, we empirically demonstrate the trade-off between privacy guarantee and prediction performance for models trained by {PriFedSync} in computer vision tasks. ",Federated $f$-Differential Privacy,1,"['New paper: In *Federated f-Differential Privacy* (<LINK>), we proposed a new privacy notion tailored to the setting where the clients ally in the attack. This privacy concept is adapted from f-differential privacy.  w/ Qinqing Zheng, @ShuxiaoC, and Qi Long.']",21,02,256