64 citations · 125 across the 18 of their papers we have counts for
Showing 2022 · cs.LGShow all
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cs.LG2022★ 5 cited
Trading Off Privacy, Utility and Efficiency in Federated Learning
Xiaojin Zhang, Yan Kang, Kai Chen +2
Federated learning (FL) enables participating parties to collaboratively build a global model with boosted utility without disclosing private data information. Appropriate protecti…
cs.LG2022★ 1 cited
No Free Lunch Theorem for Security and Utility in Federated Learning
Xiaojin Zhang, Hanlin Gu, Lixin Fan +2
In a federated learning scenario where multiple parties jointly learn a model from their respective data, there exist two conflicting goals for the choice of appropriate algorithms…
cs.LG2022★ 1 cited
PPA: Preference Profiling Attack Against Federated Learning
Chunyi Zhou, Yansong Gao, Anmin Fu +5
Federated learning (FL) trains a global model across a number of decentralized users, each with a local dataset. Compared to traditional centralized learning, FL does not require d…