scholar.google.com › citations
We propose Clustered Hierarchical Distributed Federated Learning to solve the above problems. We motivate the participation of clients by clustering.
We apply a hierarchical segmented gossip protocol and feedback mechanism for in-cluster model exchange and gossip protocol for communication between clusters to ...
People also ask
What is clustered federated learning?
What is the difference between distributed learning and federated learning?
What are the different types of federated learning algorithms?
What are the techniques used in federated learning?
Apr 24, 2020 · Abstract:Federated learning (FL) is a well established method for performing machine learning tasks over massively distributed data.
To alleviate the impact of non-IID data issue, we present an adap- tive clustered federated learning approach, 𝙰𝚍𝚊𝙲𝙵𝙻 , which can classify clients into suitable ...
” We employ clustering to alleviate the impact of Non-IID data on hierarchical federated learning. This helps ensure that training accuracy is not compromised.
Request PDF | On May 16, 2022, Yan Gou and others published Clustered Hierarchical Distributed Federated Learning | Find, read and cite all the research you ...
Mar 25, 2024 · In this paper, we propose an adaptive CFL framework, named FedAC, which (1) efficiently integrates global knowledge into intra-cluster learning.
We design a unified clustering algorithm FedUC to organize workers for different patterns. Experimental results on classical models and datasets show that ...
Clustered federated learning (CFL) clusters the devices based on the cosine similarity of their local gradients and proposes a weighted model aggregation ...
Sep 8, 2023 · This approach leverages a Hierarchical Federated Learning (HFL) framework with cluster heads to facilitate data aggregation and model training ...