64 citations · 125 across the 18 of their papers we have counts for
18 papers
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…
SoK: Fully Homomorphic Encryption Accelerators
Junxue Zhang, Xiaodian Cheng, Liu Yang +3
Fully Homomorphic Encryption~(FHE) is a key technology enabling privacy-preserving computing. However, the fundamental challenge of FHE is its inefficiency, due primarily to the un…
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…
Secure Forward Aggregation for Vertical Federated Neural Networks
Shuowei Cai, Di Chai, Liu Yang +5
Vertical federated learning (VFL) is attracting much attention because it enables cross-silo data cooperation in a privacy-preserving manner. While most research works in VFL focus…
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…
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…
Practical and Secure Federated Recommendation with Personalized Masks
Liu Yang, Junxue Zhang, Di Chai +4
Federated recommendation addresses the data silo and privacy problems altogether for recommender systems. Current federated recommender systems mainly utilize cryptographic or obfu…
HAFLO: GPU-Based Acceleration for Federated Logistic Regression
Xiaodian Cheng, Wanhang Lu, Xinyang Huang +2
In recent years, federated learning (FL) has been widely applied for supporting decentralized collaborative learning scenarios. Among existing FL models, federated logistic regress…
Practical Lossless Federated Singular Vector Decomposition over Billion-Scale Data
Di Chai, Leye Wang, Junxue Zhang +4
With the enactment of privacy-preserving regulations, e.g., GDPR, federated SVD is proposed to enable SVD-based applications over different data sources without revealing the origi…
FedEval: A Holistic Evaluation Framework for Federated Learning
Di Chai, Leye Wang, Liu Yang +3
Federated Learning (FL) has been widely accepted as the solution for privacy-preserving machine learning without collecting raw data. While new technologies proposed in the past fe…