64 citations · 125 across the 18 of their papers we have counts for
9 papers · 1 filter
A Survey for Federated Learning Evaluations: Goals and Measures
Di Chai, Leye Wang, Liu Yang +3
Evaluation is a systematic approach to assessing how well a system achieves its intended purpose. Federated learning (FL) is a novel paradigm for privacy-preserving machine learnin…
VERTICES: Efficient Two-Party Vertical Federated Linear Model with TTP-aided Secret Sharing
Mingxuan Fan, Yilun Jin, Liu Yang +2
Vertical Federated Learning (VFL) has emerged as one of the most predominant approaches for secure collaborative machine learning where the training data is partitioned by features…
Probably Approximately Correct Federated Learning
Xiaojin Zhang, Anbu Huang, Lixin Fan +2
Federated learning (FL) is a new distributed learning paradigm, with privacy, utility, and efficiency as its primary pillars. Existing research indicates that it is unlikely to sim…
A Meta-learning Framework for Tuning Parameters of Protection Mechanisms in Trustworthy Federated Learning
Xiaojin Zhang, Yan Kang, Lixin Fan +2
Trustworthy Federated Learning (TFL) typically leverages protection mechanisms to guarantee privacy. However, protection mechanisms inevitably introduce utility loss or efficiency…
Theoretically Principled Federated Learning for Balancing Privacy and Utility
Xiaojin Zhang, Wenjie Li, Yiming Li +3
We propose a general learning framework for the protection mechanisms that protects privacy via distorting model parameters, which facilitates the trade-off between privacy and uti…
Towards Achieving Near-optimal Utility for Privacy-Preserving Federated Learning via Data Generation and Parameter Distortion
Xiaojin Zhang, Kai Chen, Qiang Yang
Federated learning (FL) enables participating parties to collaboratively build a global model with boosted utility without disclosing private data information. Appropriate protecti…
A Survey on Vertical Federated Learning: From a Layered Perspective
Liu Yang, Di Chai, Junxue Zhang +6
Vertical federated learning (VFL) is a promising category of federated learning for the scenario where data is vertically partitioned and distributed among parties. VFL enriches th…
A Game-theoretic Framework for Privacy-preserving Federated Learning
Xiaojin Zhang, Lixin Fan, Siwei Wang +3
In federated learning, benign participants aim to optimize a global model collaboratively. However, the risk of \textit{privacy leakage} cannot be ignored in the presence of \texti…
Federated Learning without Full Labels: A Survey
Yilun Jin, Yang Liu, Kai Chen +1
Data privacy has become an increasingly important concern in real-world big data applications such as machine learning. To address the problem, federated learning (FL) has been a p…