Federated learning (FL) is a distributed learning method that offers medical institutes the prospect of collaboration in a global model while preserving the privacy of their patients. Although most medical centers conduct similar medical imaging tasks, their differences, such as specializations, number ...
In Case 4, data samples with the first half of the labels are distributed to the first half of the nodes as in Case 1; Fig. 4. Loss function values and classification accuracy with different τ. Only SVM and CNN classifiers have accuracy values. The curves show the resultsfrom the baseli...
【流行前沿】DRAG Divergence-based Adaptive Aggregation in Federated learning on Non-IID Data 今天再分享一篇9月的联邦学习领域处理异构数据分布的文章。看挂名是复旦的王昕,总的来说只能算是踏实的工作,但是新意上确实不太够。 文章的主要处理对象是解决异构数据在联邦训练中的client-drift问题,当然与很多相似论文...
Experimental results demonstrate that our proposed Federated Learning method significantly outperforms existing methods in variety image classification tasks, achieving faster model convergence and superior performance when dealing with non-IID data distributions. 展开 关键词: Federated Learning Non-IID data ...
Recently, federated learning (FL) is gaining popularity since it allows researchers to train powerful models without compromising data privacy and security. However, the performance of existing FL approaches often deteriorates when encountering non-iid situations where there exist ...
To overcome these limitations, we proposed a new approach to intrusion detection in large-scale local area networks (LANs). We proposed segmented federated learning (Segmented-FL), a machine learning technique that enables collaborative training of models on non-IID data sources without sharing sensit...
Personalized Federated Learning with Adaptive Feature Extraction and Category Prediction in Non-IID Datasets Federated learning trains a neural network model using the client鈥檚 data to maintain the benefits of centralized model training while maintaining their p... YH Lai,SY Chen,WC Chou,... 被...
Feddc: Federated learning with non-iid data via local drift decoupling and correction. In Proceed- ings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 10112–10121, 2022. [10] Robert Geirhos, Patricia Rubisch, Claudio Michaelis, Matthias Bethge,...
Federated Continual Learning This is an official implementation of Federated Continual Learning with Adaptive Parameter Communication (paper). We propose a novel federated continual learning framework, Federated continual learning with Adaptive Parameter Communication (Fed-APC), which additively decomposes the ...
Moreover, the global model trained with heterogeneous data can be biasedtowards some of the participants, while performing poorly for others ([21]). This is known as unfairness problem infederated learning. There are ways to improve fairness in federated learning, at the cost of model ...