文章:Towards Personalized Federated Learning 作者:Alysa Ziying Tan, Han Yu∗, Lizhen Cui∗, and Qiang Yang∗ 机构:Alysa Ziying Tan is with the School of Computer Science and Engineering, Nanyang Technological University, Singapore; Alibaba-NTU Singapore Joint Research Institute, NTU, Singapore...
摘要Federated Learning (FL) aims to develop a centralized server that learns from distributed clients via communications without accessing the clients’ local data. However, existing works mainly focus on federated learning in a single task sce- nario with static data. In this paper, we introduce ...
Personalized Federated Learning: A Meta-Learning Approach Towards Federated Learning: Robustness Analytics to Data Heterogeneity Highlight: non-IID + adversarial attacks Salvaging Federated Learning by Local Adaptation Highlight: an experimental paper that evaluate FL can help to improve the local accuracy...
is a distributed machine learning approach that enables model training on a large corpus of decentra...
The purpose of this research paper is to investigate an innovative application of federated learning with convolutional neural networks (CNN) for the classification of tea leaf diseases. Based on aggregating data from four clients, we depict a variety of disease appearances based on four distinct deg...
Federated learning is a privacy-preserving machine learning technique to train intelligent models from decentralized data, which enables exploiting private data by communicating local model updates in each iteration of model learning rather than the raw data. However, model updates can be extremely large...
The artificial intelligence revolution has been spurred forward by the availability of large-scale datasets. In contrast, the paucity of large-scale medical datasets hinders the application of machine learning in healthcare. The lack of publicly availabl
federated learning (FL) has emerged as a promising solution, allowing privacy preservation by training models locally and exchanging them to improve overall performance. Additionally, the computational load is distributed across multiple clients, reducing the burden on central servers. This paper presents...
(see Figure 3-a), indicating that the local model can distill the refined knowledge of the global model. FedX-enhanced models also have larger inter-class angles, demonstrating better class discrimination (see Figure 3-b). The paper “FedX: Unsupervised Feder...
Yang Liu, Qiang Yang, Tianjian Chen, and Zhuoshi Wei, "Federated Learning: User Privacy, Data Security and Confidentiality in Machine Learning," Jan. 2019. Available:https://img.fedai.org.cn/fedweb/1552916850679.pdf H. Brendan McMahan, "Federated Learning: From Research to Practice," Sep....