Zobrazeno 1 - 10
of 87
pro vyhledávání: '"Chai, Di"'
Autor:
Yang, Liu, Cai, Shuowei, Chai, Di, Zhang, Junxue, Tian, Han, Jin, Yilun, Guo, Kun, Chen, Kai, Yang, Qiang
As an essential tool of secure distributed machine learning, vertical federated learning (VFL) based on homomorphic encryption (HE) suffers from severe efficiency problems due to data inflation and time-consuming operations. To this core, we propose
Externí odkaz:
http://arxiv.org/abs/2405.00482
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 learning that allows multiple parties to collaboratively train models without
Externí odkaz:
http://arxiv.org/abs/2308.11841
Spatiotemporal crowd flow prediction is one of the key technologies in smart cities. Currently, there are two major pain points that plague related research and practitioners. Firstly, crowd flow is related to multiple domain knowledge factors; howev
Externí odkaz:
http://arxiv.org/abs/2306.04144
Autor:
Yang, Liu, Chai, Di, Zhang, Junxue, Jin, Yilun, Wang, Leye, Liu, Hao, Tian, Han, Xu, Qian, Chen, Kai
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 the description of samples using features from different parties to impr
Externí odkaz:
http://arxiv.org/abs/2304.01829
Autor:
Cai, Shuowei, Chai, Di, Yang, Liu, Zhang, Junxue, Jin, Yilun, Wang, Leye, Guo, Kun, Chen, Kai
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 on linear and tree models, deep models (e.g., neural networks) are no
Externí odkaz:
http://arxiv.org/abs/2207.00165
Autor:
Chai, Di, Chen, Yihan, Jiang, Zelun, Li, Zhihao, Zhang, Xiangyu, Chen, Xing, Wang, Yi, Cui, Kangping
Publikováno v:
In Separation and Purification Technology 1 October 2024 345
Federated recommendation addresses the data silo and privacy problems altogether for recommender systems. Current federated recommender systems mainly utilize cryptographic or obfuscation methods to protect the original ratings from leakage. However,
Externí odkaz:
http://arxiv.org/abs/2109.02464
Vertical federated learning (VFL) leverages various privacy-preserving algorithms, e.g., homomorphic encryption or secret sharing based SecureBoost, to ensure data privacy. However, these algorithms all require a semi-honest secure definition, which
Externí odkaz:
http://arxiv.org/abs/2108.06958
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 original data. However, many SVD-based applications cannot be well supporte
Externí odkaz:
http://arxiv.org/abs/2105.08925
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 few years do evolve the FL area, unfortunately, the evaluation results p
Externí odkaz:
http://arxiv.org/abs/2011.09655