数据量异构性提升神经影像联邦学习性能
Data Amount Heterogeneity Improves Performance in Federated Learning for Neuroimaging
做医学影像或联邦学习的朋友可以看看：这篇论文说一个大数据站配小站点，比大家数据量均分训练效果更好，小站点数据也能被充分利用。
arXiv 论文研究联邦学习（FL）在神经影像任务中的应用，例如基于结构 MRI 的阿尔茨海默病分类。作者发现由 1 个大数据站点加多个小数据站点组成的异构联邦，比各站点数据量相同的同构联邦表现更好，在低数据量场景下优势尤其明显。大数据站点的存在带来更快的收敛速度和更高的最终准确率，整体性能接近集中式训练。这一发现适用于 ENIGMA 等包含大量小数据站点的联盟，模型参数通过同态加密传输，数据不出本地。
Data Amount Heterogeneity Improves Performance in Federated Learning for Neuroimaging
Federated Learning (FL) allows for secure analysis of distributed data without data sharing. We and others have successfully applied FL to neuroimaging tasks, such as Alzheimer's disease classification from structural magnetic resonance imaging (MRI). FL trains a deep learning model locally at each site, encrypts the local model parameters and sends them to a federation controller that aggregates the local models under homomorphic encryption, the resulting community model is sent back to the sites for further training, and the process repeats for a number of rounds. FL often achieves learning performance (e.g., accuracy) close to centralized training, but no data leaves the sites and model parameters are protected through encryption and other measures. In this paper, we show that federations composed of sites with heterogeneous amounts of data (1 large site plus small sites) perform better than homogeneous (same data per site) federations, particularly in low data regimes, while reaching performance close to centralized training. Having a big site often results in faster convergence and higher final accuracy. Heterogeneous federations better exploit sites with small amounts of data, a common situation in large consortia such as ENIGMA.