论文笔记:ICML'21 SpreadGNN: Serverless Multi task Federated Learning for Graph Neural Networks 天下客 机器学习、联邦学习、图神经网络 来自专栏 · FL-Graph 14 人赞同了该文章 前言 GNN 是分析解决图机器学习问题的首选方法,但是由于用户方的隐私问题、法规限制和商业竞争
Federated Multi-Task Learning. Contribute to gingsmith/fmtl development by creating an account on GitHub.
我们交替使用task和class这个词,每个标签由多个任务组成。首先,我们提出了一种多任务学习(MTL)公式来从部分标签中学习。其次,在我们的MTL公式中,我们利用分散周期平均随机梯度下降(DPA-SGD)来解决无服务器MTL优化问题,并为DPA-SGD的收敛性提供了理论保证,这进一步验证了我们设计的合理性。(本文做了什么) 我们在图级...
Clustered Federated Learning: Model-Agnostic Distributed Multi-Task Optimization under Privacy Constraints - felisat/clustered-federated-learning
Jiayi Chen and Aidong Zhang. 2022. FedMSplit: Correlation-Adaptive Federated Multi-Task Learning across Multimodal Split Networks. In Proceedings of the 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD '22). Association for Computing Machinery, New York, NY, USA, 87–96....
[6]. Martín Abadi, Andy Chu, Ian Goodfellow, H. Brendan McMahan, Ilya Mironov, Kunal Talwar, Li Zhang.Deep Learning with Differential Privacy.2016 [7]. Virginia Smith, Chao-Kai Chiang, Maziar Sanjabi, Ameet Talwalkar.Federated Multi-Task Learning2016 ...
Federated learning has been applied to train different tasks, posing new computation challenges in training, especially when the scenario becomes multi-task. In this paper, we profile the FL multi-task training process at the operator-level to identify and solve the p...
联邦多任务学习(Federated MultiTask Learning):联邦学习在考虑到本地数据隐私的大量移动设备上提供协作训练ML模型。这种设置也可以扩展到联合多任务学习环境,在该环境中,多任务学习驱动个性化但共享的设备模型。 可信执行环境(Trusted Execution Environment,TEE):这种技术也被用于不同的ML模型的隐私保护,其中计算资源的私...
Federated learning (FL) for medical image segmentation becomes more challenging in multi-task settings where clients might have different categories of labels represented in their data. For example, one client might have patient data with “healthy”...
In this paper, we investigate multi-task OtA FL in Cell-free mMIMO systems. We propose optimal designs of transmit coefficients and receive combining at different levels of cooperation among the access points, aiming to minimize the sum of OtA model aggregation errors across all FL groups. ...