10 citations · 10 across the 1 of their papers we have counts for
12 papers · 1 filter
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…
FilterFL: Knowledge Filtering-based Data-Free Backdoor Defense for Federated Learning
Yanxin Yang, Ming Hu, Xiaofei Xie +4
As a distributed machine learning paradigm, Federated Learning (FL) enables large-scale clients to collaboratively train a model without sharing their raw data. However, due to the…
Gradients as an Action: Towards Communication-Efficient Federated Recommender Systems via Adaptive Action Sharing
Zhufeng Lu, Chentao Jia, Ming Hu +2
As a promising privacy-aware collaborative model training paradigm, Federated Learning (FL) is becoming popular in the design of distributed recommender systems. However, Federated…
FedQP: Towards Accurate Federated Learning using Quadratic Programming Guided Mutation
Jiawen Weng, Zeke Xia, Ran Li +2
Due to the advantages of privacy-preserving, Federated Learning (FL) is widely used in distributed machine learning systems. However, existing FL methods suffer from low-inference…
CyclicFL: A Cyclic Model Pre-Training Approach to Efficient Federated Learning
Pengyu Zhang, Yingbo Zhou, Ming Hu +2
Federated learning (FL) has been proposed to enable distributed learning on Artificial Intelligence Internet of Things (AIoT) devices with guarantees of high-level data privacy. Si…
CaBaFL: Asynchronous Federated Learning via Hierarchical Cache and Feature Balance
Zeke Xia, Ming Hu, Dengke Yan +5
Federated Learning (FL) as a promising distributed machine learning paradigm has been widely adopted in Artificial Intelligence of Things (AIoT) applications. However, the efficien…