ps: another research direction is personalized federated learning, which tries to learn personalized local models for each party. 2.2 contrastive learning the key idea of contrastive learning is toreduce the distancebetween the representations of different augmented views ofthe same image(positive pairs)...
federated learning是一种训练数据去中心化的机器学习解决方案,最早于2016年由谷歌公司提出,目的在于通过对保存在大量终端的分布式数据开展训练学习一个高质量中心化的机器学习模型,解决数据孤岛的问题。 Federated Learning示意图 federated learning不断循环以下步骤,直至训练出最终模型: 在符合条件的用户集合中挑选出部分用...
While security is at the core of FL, there are still many articles referred to distributed machine learning with no security guarantee as “federated learning”, which are not satisfied with the FL definition supposed to be. To this end, in this paper, we reiterate the concept of federated ...
Federated learning is a type of distributed machine learning where machine learning and deep learning algorithms are trained on data from edge devices like laptops, smartphones, and wearable devices, without the need to transfer the data to a central server. This approach helps meet latency requirem...
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...
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 ...
Explore additional research papers on the following topics: For Large Language Models papers, please visit the LLM Repository. For Backdoor Learning papers, please visit the Backdoor Learning Repository. For Federated Learning papers, please visit the Federated Learning Repository. For Machine Unlearning...
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
Paper A Survey on Federated Learning Systems: Vision, Hype and Reality for Data Privacy and Protection Federated learning has been a hot research topic in enabling the collaborative training of machine learning models among different organizations under the privacy restrictions. As researchers try to ...
Research in the food domain is at times limited due to data sharing obstacles, such as data ownership, privacy requirements, and regulations. While important, these obstacles can restrict data-driven methods suc