20 citations · 62 across the 10 of their papers we have counts for
10 papers
A Survey on Contribution Evaluation in Vertical Federated Learning
Yue Cui, Chung-ju Huang, Yuzhu Zhang +4
Vertical Federated Learning (VFL) has emerged as a critical approach in machine learning to address privacy concerns associated with centralized data storage and processing. VFL fa…
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…
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…
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…
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…
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…