1.2k citations · 1.8k across the 28 of their papers we have counts for
8 papers · 1 filter
The Prospect of Enhancing Large-Scale Heterogeneous Federated Learning with Transformers
Yulan Gao, Zhaoxiang Hou, Chengyi Yang +2
Federated learning (FL) addresses data privacy concerns by enabling collaborative training of AI models across distributed data owners. Wide adoption of FL faces the fundamental ch…
Aggregating Intrinsic Information to Enhance BCI Performance through Federated Learning
Rui Liu, Yuanyuan Chen, Anran Li +3
Insufficient data is a long-standing challenge for Brain-Computer Interface (BCI) to build a high-performance deep learning model. Though numerous research groups and institutes co…
Fairness-Aware Client Selection for Federated Learning
Yuxin Shi, Zelei Liu, Zhuan Shi +1
Federated learning (FL) has enabled multiple data owners (a.k.a. FL clients) to train machine learning models collaboratively without revealing private data. Since the FL server ca…
Multi-Tier Client Selection for Mobile Federated Learning Networks
Yulan Gao, Yansong Zhao, Han Yu
Federated learning (FL), which addresses data privacy issues by training models on resource-constrained mobile devices in a distributed manner, has attracted significant research a…
FedGH: Heterogeneous Federated Learning with Generalized Global Header
Liping Yi, Gang Wang, Xiaoguang Liu +2
Federated learning (FL) is an emerging machine learning paradigm that allows multiple parties to train a shared model collaboratively in a privacy-preserving manner. Existing horiz…
Efficient Training of Large-scale Industrial Fault Diagnostic Models through Federated Opportunistic Block Dropout
Yuanyuan Chen, Zichen Chen, Sheng Guo +6
Artificial intelligence (AI)-empowered industrial fault diagnostics is important in ensuring the safe operation of industrial applications. Since complex industrial systems often i…
FedSDG-FS: Efficient and Secure Feature Selection for Vertical Federated Learning
Anran Li, Hongyi Peng, Lan Zhang +4
Vertical Federated Learning (VFL) enables multiple data owners, each holding a different subset of features about largely overlapping sets of data sample(s), to jointly train a use…
Towards Interpretable Federated Learning
Anran Li, Rui Liu, Ming Hu +4
Federated learning (FL) enables multiple data owners to build machine learning models collaboratively without exposing their private local data. In order for FL to achieve widespre…