参与方们的数据的特征空间和样本空间也许不是完全相同的,所以我们可以基于数据在不同参与方(parties)的特征空间和样本 ID 空间上的分布情况,将联邦学习分为水平联邦学习(horizontal federated learing)、垂直联邦学习(vertical federated learning)和联邦迁移学习(federated transfer learning)。 水平联邦学习(horizontal feder...
联邦机器学习(Federated machine learning/Federated Learning),又名联邦学习,联合学习,联盟学习。联邦机器学习是一个机器学习框架,能有效帮助多个机构在满足用户隐私保护、数据安全和政府法规的要求下,进行数据使用和机器学习建模。 举例来说,假设有两个不同的企业A 和 B,它们拥有不同数据。比如,企业 A 有用户特征数据...
Privacy of Federated Learning A Categorization of Federated Learning Architecture for a federated learning system RELATED WORKS Privacy-preserving machine learning Federated Learning vs Distributed Machine Learning Federated Learning vs Edge Computing Federated Learning vs Federated Database Systems APPLICATIONS F...
[1] Martin Abadi, Andy Chu, Ian Goodfellow, H. Brendan McMahan, Ilya Mironov, Kunal Talwar, and Li Zhang. 2016. Deep Learning with Differential Privacy. In Proceedings of the 2016 ACM SIGSAC Conference on Computer and Communications Security (CCS ’16). ACM, New York, NY, USA, 308–318...
基于各方数据在特征和样本ID空间的分布规律,联邦学习被分为横向联邦学习(horizontally federated learning)、纵向联邦学习(vertically federated learning)和联邦迁移学习(federated transfer learning,FTL) 横向联邦学习 基于样本的联邦学习 数据集享有相同的特征空间,但其样本有差别。即Xi=Xj, Yi=Yj, Ii≠ Ij, ∀Di...
machine learning tasks. Federated learning emerges as an efficient approach to exploit distributed data and computing resources, so as to collaboratively train machine learning models. At the same time, federated learning obeys the laws and regulations and ensures data security and data privacy. In ...
Although federated learning is often seen as a promising solution to allow AI innovation while addressing privacy concerns, we argue that this technology does not fix all underlying data ethics concerns. Benefiting from federated learning in digital heal
The broad application of artificial intelligence techniques in medicine is currently hindered by limited dataset availability for algorithm training and validation, due to the absence of standardized electronic medical records, and strict legal and ethic
2 A Federated Random Forest Solution for Secure Distributed Machine Learning Alexandre Cotorobai, Jorge Miguel Silva, Jose Luis Oliveira 2025-05-12 arXiv https://github.com/ieeta-pt/fed_rf http://arxiv.org/abs/2505.08085v1 3 FNBench: Benchmarking Robust Federated Learning against Noisy Labels...
Awesome Federated Machine Learning Federated Learning (FL) is a new machine learning framework, which enables multiple devices collaboratively to train a shared model without compromising data privacy and security. This repository aims to keep tracking the latest research advancements of federated learning...