41 citations · 107 across the 6 of their papers we have counts for
6 papers
Lotto: Secure Participant Selection against Adversarial Servers in Federated Learning
Zhifeng Jiang, Peng Ye, Shiqi He +3
In Federated Learning (FL), common privacy-enhancing techniques, such as secure aggregation and distributed differential privacy, rely on the critical assumption of an honest major…
Feature Reconstruction Attacks and Countermeasures of DNN training in Vertical Federated Learning
Peng Ye, Zhifeng Jiang, Wei Wang +2
Federated learning (FL) has increasingly been deployed, in its vertical form, among organizations to facilitate secure collaborative training over siloed data. In vertical FL (VFL)…
Dordis: Efficient Federated Learning with Dropout-Resilient Differential Privacy
Zhifeng Jiang, Wei Wang, Ruichuan Chen
Federated learning (FL) is increasingly deployed among multiple clients to train a shared model over decentralized data. To address privacy concerns, FL systems need to safeguard t…
Pisces: Efficient Federated Learning via Guided Asynchronous Training
Zhifeng Jiang, Wei Wang, Baochun Li +1
Federated learning (FL) is typically performed in a synchronous parallel manner, where the involvement of a slow client delays a training iteration. Current FL systems employ a par…
FLASHE: Additively Symmetric Homomorphic Encryption for Cross-Silo Federated Learning
Zhifeng Jiang, Wei Wang, Yang Liu
Homomorphic encryption (HE) is a promising privacy-preserving technique for cross-silo federated learning (FL), where organizations perform collaborative model training on decentra…
Towards Efficient Synchronous Federated Training: A Survey on System Optimization Strategies
Zhifeng Jiang, Wei Wang, Bo Li +1
The increasing demand for privacy-preserving collaborative learning has given rise to a new computing paradigm called federated learning (FL), in which clients collaboratively trai…