358 citations · 368 across the 4 of their papers we have counts for
4 papers
FedMP: Tackling Medical Feature Heterogeneity in Federated Learning from a Manifold Perspective
Zhekai Zhou, Shudong Liu, Zhaokun Zhou +4
Federated learning (FL) is a decentralized machine learning paradigm in which multiple clients collaboratively train a shared model without sharing their local private data. Howeve…
Adaptive Guidance for Local Training in Heterogeneous Federated Learning
Jianqing Zhang, Yang Liu, Yang Hua +2
Model heterogeneity poses a significant challenge in Heterogeneous Federated Learning (HtFL). In scenarios with diverse model architectures, directly aggregating model parameters i…
Vertical Federated Learning: Concepts, Advances and Challenges
Yang Liu, Yan Kang, Tianyuan Zou +6
Vertical Federated Learning (VFL) is a federated learning setting where multiple parties with different features about the same set of users jointly train machine learning models w…
Batch Label Inference and Replacement Attacks in Black-Boxed Vertical Federated Learning
Yang Liu, Tianyuan Zou, Yan Kang +4
In a vertical federated learning (VFL) scenario where features and model are split into different parties, communications of sample-specific updates are required for correct gradie…