200 citations · 569 across the 8 of their papers we have counts for
8 papers
ML-Asset Management: Curation, Discovery, and Utilization
Mengying Wang, Moming Duan, Yicong Huang +3
Machine learning (ML) assets, such as models, datasets, and metadata, are central to modern ML workflows. Despite their explosive growth in practice, these assets are often underut…
Towards Open Federated Learning Platforms: Survey and Vision from Technical and Legal Perspectives
Moming Duan, Qinbin Li, Linshan Jiang +1
Traditional Federated Learning (FL) follows a server-dominated cooperation paradigm which narrows the application scenarios of FL and decreases the enthusiasm of data holders to pa…
Data-Free Diversity-Based Ensemble Selection For One-Shot Federated Learning in Machine Learning Model Market
Naibo Wang, Wenjie Feng, Fusheng Liu +2
The emerging availability of trained machine learning models has put forward the novel concept of Machine Learning Model Market in which one can harness the collective intelligence…
Flexible Clustered Federated Learning for Client-Level Data Distribution Shift
Moming Duan, Duo Liu, Xinyuan Ji +4
Federated Learning (FL) enables the multiple participating devices to collaboratively contribute to a global neural network model while keeping the training data locally. Unlike th…
CSAFL: A Clustered Semi-Asynchronous Federated Learning Framework
Yu Zhang, Moming Duan, Duo Liu +5
Federated learning (FL) is an emerging distributed machine learning paradigm that protects privacy and tackles the problem of isolated data islands. At present, there are two main…
FedSAE: A Novel Self-Adaptive Federated Learning Framework in Heterogeneous Systems
Li Li, Moming Duan, Duo Liu +5
Federated Learning (FL) is a novel distributed machine learning which allows thousands of edge devices to train model locally without uploading data concentrically to the server. B…
FedGroup: Efficient Clustered Federated Learning via Decomposed Data-Driven Measure
Moming Duan, Duo Liu, Xinyuan Ji +4
Federated Learning (FL) enables the multiple participating devices to collaboratively contribute to a global neural network model while keeping the training data locally. Unlike th…
Astraea: Self-balancing Federated Learning for Improving Classification Accuracy of Mobile Deep Learning Applications
Moming Duan, Duo Liu, Xianzhang Chen +4
Federated learning (FL) is a distributed deep learning method which enables multiple participants, such as mobile phones and IoT devices, to contribute a neural network model while…