In this fine-tuned multi-objective framework, the primary objective is established as the common global optimization objective in federated learning, specifically the expected empirical loss across all client datasets. The sub-objective is set as alleviating the gradient conflict between the global ...
2.Multi-objective Minimization的FL AFL的优化目标,第一篇文章。 作者说这个很像Multi-objective minimization (MoM)中的 Chebyshev approach,于是提出如下iterative的算法,这里adaptively “center” the user functions using function values from the previous iteration. 当函数f是光滑的时候,用quadratic bound得到: 强...
《2022-IEEE-FedMGDA+: Federated Learning meets Multi-objective Optimization》同样关注性能最差的设备,为了兼顾公平性和鲁棒性,提出了 FedMGDA+方法。通过修改参与者梯度合并的权重来改进联邦模型公平性。多目标优化更关心当前模型在所有参与者的结果,它使用帕累托稳定(Pareto-stationary)解决方案。《2021-NIPS-...
This chapter presents two multi-objective evolutionary algorithms for federated neural architecture search. The first one employs a probabilistic representation of deep neural architectures that describes the connectivity between two neighboring layers and simultaneously maximizing the performance and minimizing ...
并在Springer上发表了他们的研究结果:《Multi-institutional Deep Learning Modeling Without Sharing Patient Data: A Feasibility Study on Brain Tumor Segmentation》 如上图,使用Intel硬件的联盟学习架构。加密模型被发送到各个机构(数据所有者AC),这些机构在硬件中的安全区域内解密,然后训练本地数据。仅与中央模型...
learning(DML)to transfer knowledge between these two models on local data.To overcome objective heterogeneity(OH),we design a shared global model that includes only certain parts,and the personalized model is task-specific and enhanced through mutual learning with the meme model.We evaluate the ...
并在Springer上发表了他们的研究结果:《Multi-institutional Deep Learning Modeling Without Sharing ...
High Dimensional Restrictive Federated Model Selection with multi-objective Bayesian Optimization over shifted distributions Client Selection for Federated Learning with Heterogeneous Resources in Mobile Edge Federated Meta-Learning with Fast Convergence and Efficient Communication Robust Federated Learning Through Re...
(1)横向联邦学习。横向联邦学习又称为特征对齐的联邦学习(Feature-Aligned Federated Learning),是把...
我非常看好Federated Learning的前景,所以我投入了一半的科研时间。最近我们发布了FedML开源框架,我们支持机房分布式、边缘设备训练、单机模拟三种计算范式。希望可以进一步的推进这个方向的落地与应用。FedML的官方网站是fedml.ai我特地录制了视频介绍,讲解了我们FedML的设计思路,也提供了入门算法。希望对你理解FL的前景有帮...