2 code implementations • 1 Jan 2024 • Zhiyuan Wu, Tianliu He, Sheng Sun, Yuwei Wang, Min Liu, Bo Gao, Xuefeng Jiang
Federated Learning (FL) enables collaborative model training among participants while guaranteeing the privacy of raw data.
1 code implementation • 1 Dec 2023 • Zhiyuan Wu, Sheng Sun, Yuwei Wang, Min Liu, Bo Gao, Quyang Pan, Tianliu He, Xuefeng Jiang
Federated Learning (FL) enables training Artificial Intelligence (AI) models over end devices without compromising their privacy.
no code implementations • 14 Nov 2023 • Yuwei Wang, Runhan Li, Hao Tan, Xuefeng Jiang, Sheng Sun, Min Liu, Bo Gao, Zhiyuan Wu
By fusing the logits of the two models, the private weak learner can capture the variance of different data, regardless of their category.
no code implementations • 14 Jul 2023 • Jingjing Xue, Min Liu, Sheng Sun, Yuwei Wang, Hui Jiang, Xuefeng Jiang
In this paper, we propose Federated learning with Bayesian Inference-based Adaptive Dropout (FedBIAD), which regards weight rows of local models as probability distributions and adaptively drops partial weight rows based on importance indicators correlated with the trend of local training loss.
2 code implementations • 9 May 2023 • Nannan Wu, Li Yu, Xuefeng Jiang, Kwang-Ting Cheng, Zengqiang Yan
Federated noisy label learning (FNLL) is emerging as a promising tool for privacy-preserving multi-source decentralized learning.
1 code implementation • 14 Jan 2023 • Zhiyuan Wu, Sheng Sun, Yuwei Wang, Min Liu, Xuefeng Jiang, Runhan Li, Bo Gao
The increasing demand for intelligent services and privacy protection of mobile and Internet of Things (IoT) devices motivates the wide application of Federated Edge Learning (FEL), in which devices collaboratively train on-device Machine Learning (ML) models without sharing their private data.
1 code implementation • 1 Jan 2023 • Zhiyuan Wu, Sheng Sun, Yuwei Wang, Min Liu, Quyang Pan, Xuefeng Jiang, Bo Gao
Federated Multi-task Learning (FMTL) is proposed to train related but personalized ML models for different devices, whereas previous works suffer from excessive communication overhead during training and neglect the model heterogeneity among devices in MEC.
1 code implementation • 25 Aug 2022 • Xuefeng Jiang, Sheng Sun, Yuwei Wang, Min Liu
Federated learning (FL) aims to learn joint knowledge from a large scale of decentralized devices with labeled data in a privacy-preserving manner.