237 citations · 358 across the 20 of their papers we have counts for
12 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…
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…