Federated Learning (FL), as a privacy-preserving distributed machine learning paradigm, has become a promising privacy computing framework for increasingly complex network …
In this paper, we study the problem of privacy-preserving data synthesis (PPDS) for tabular data in a distributed multi-party environment. In a decentralized setting, for PPDS, federated …
S Ke, C Huang, X Liu - arXiv preprint arXiv:2211.07816, 2022 - arxiv.org
Federated Learning (FL) is a distributed machine learning paradigm where clients collaboratively train a model using their local (human-generated) datasets. While existing …
S Lin, D Zhai, F Zhang, J Jiang, X Liu, X Ji - Machine Intelligence Research, 2024 - Springer
Federated learning (FL) is a promising decentralized machine learning approach that enables multiple distributed clients to train a model jointly while keeping their data private …
Federated learning has gained popularity for distributed learning without aggregating sensitive data from clients. But meanwhile, the distributed and isolated nature of data …
Y Xu, Y Liao, L Wang, H Xu, Z Jiang… - IEEE Transactions on …, 2024 - ieeexplore.ieee.org
Federated learning (FL) enables edge devices to cooperatively train models without exposing their raw data. However, implementing a practical FL system at the network edge …
S Ke, C Huang, X Liu - … on Modeling and Optimization in Mobile …, 2023 - ieeexplore.ieee.org
Federated Learning (FL) is a distributed machine learning paradigm where clients collaboratively train a model using their local datasets. While existing studies focus on FL …
A Majeed, SO Hwang - IEEE Access, 2024 - ieeexplore.ieee.org
Federated learning (FL) is considered a de facto standard for privacy preservation in AI environments because it does not require data to be aggregated in some central place to …
Federated Learning (FL) is a distributed machine learning paradigm that enables learning models from decentralized private datasets where the labeling effort is entrusted to the …