2 code implementations • 5 Jul 2023 • Moming Duan, Qinbin Li, Linshan Jiang, Bingsheng He
To fully unleash the potential of FL, we advocate rethinking the design of current FL frameworks and extending it to a more generalized concept: Open Federated Learning Platforms, positioned as a crowdsourcing collaborative machine learning infrastructure for all Internet users.
1 code implementation • 23 Feb 2023 • Naibo Wang, Wenjie Feng, Fusheng Liu, Moming Duan, See-Kiong Ng
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 of multiple well-trained models to improve the performance of the resultant model through one-shot federated learning and ensemble learning in a data-free manner.
1 code implementation • 22 Aug 2021 • Moming Duan, Duo Liu, Xinyuan Ji, Yu Wu, Liang Liang, Xianzhang Chen, Yujuan Tan
Federated Learning (FL) enables the multiple participating devices to collaboratively contribute to a global neural network model while keeping the training data locally.
no code implementations • 16 Apr 2021 • Yu Zhang, Moming Duan, Duo Liu, Li Li, Ao Ren, Xianzhang Chen, Yujuan Tan, Chengliang Wang
Asynchronous FL has a natural advantage in mitigating the straggler effect, but there are threats of model quality degradation and server crash.
no code implementations • 15 Apr 2021 • Li Li, Moming Duan, Duo Liu, Yu Zhang, Ao Ren, Xianzhang Chen, Yujuan Tan, Chengliang Wang
In our framework, the server evaluates devices' value of training based on their training loss.
3 code implementations • 14 Oct 2020 • Moming Duan, Duo Liu, Xinyuan Ji, Renping Liu, Liang Liang, Xianzhang Chen, Yujuan Tan
In this paper, we propose a novel clustered federated learning (CFL) framework FedGroup, in which we 1) group the training of clients based on the similarities between the clients' optimization directions for high training performance; 2) construct a new data-driven distance measure to improve the efficiency of the client clustering procedure.
1 code implementation • 2 Jul 2019 • Moming Duan, Duo Liu, Xianzhang Chen, Yujuan Tan, Jinting Ren, Lei Qiao, Liang Liang
However, unlike the common training dataset, the data distribution of the edge computing system is imbalanced which will introduce biases in the model training and cause a decrease in accuracy of federated learning applications.