… Moreover, this paper introduces the chaos system into the federated learning field, which is
… Clustered federated learning: Model-agnostic distributed multitask optimization under privacy …

… Secondly, federated learning algorithms were divided into three categories according to …
Clustered federated learning: model-agnostic distributed multitask optimization under privacy …

… 的聚 类联邦学习(ACFL, adaptive clustering federated learning)算法.ACFL 试图利用正向激励
… Clustered federated learning:model-agnostic distributed multitask optimization under privacy …

… clusters by spectral clustering under cloud-edge collaboration. Next, a local federated learning
… method is proposed to adapt the global model to each distributed client. In this method, …

… search and progress of federated learning methods in recent … with theoretical guarantees:
A model- agnostic meta- learning … An efficient framework for clustered federated learning[J]. Ad…

… Abstract: To relieve the impact of data heterogeneity problems caused by full overlapping
attribute skew between clients in Federated Learning (FL), a local adaptive FL algorithm that …

… ) Abstract: Federated learning is a distributed machine learning approach that enables …
without compromising the privacy and security of local data. However, federated learning faces a …

… of federated learning: privacy protection, data heterogeneity … Federated optimization:
Distributed optimization beyond the … federated learning personalization via model agnostic meta …

: 边缘计算任务卸载和资源调度是当前计算机科学与信息技术领域中备受关注的研究方向之一,
是具有广阔前景的研究方向. 目前, 尚未有文献对边缘计算任务卸载和资源调度的方法在时延, 能…