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...
paper链接:Towards Personalized Federated Learning | IEEE Journals & Magazine | IEEE Xplore 正文: 这篇文章回顾了联邦学习Federated Learning的提出FedAvg,然后指出FedAvg在noniid的数据下,会发生client drift的问题,也就是global model会因为每个client的heterogenous data问题,造成global model的收敛点与local model的收...
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...
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...
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
(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 Federa...
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 such as machine learning. Federated learning, the approach of training models on locally ...