Apr 28, 2022 · We develop an algorithm that allows each cluster to communicate independently and derive the convergence results. We study a hierarchical linear ...
We develop an algorithm that allows each cluster to communicate independently and derive the convergence results. We study a hierarchical linear model to ...
Apr 28, 2022 · Abstract. We consider the problem of personalized federated learning when there are known cluster structures within users.
An algorithm is developed that allows each cluster to communicate independently and derive the convergence results, and a hierarchical linear model is ...
Personalized Federated Learning with Multiple Known Clusters · General Helper Functions · Code for Simulation Studies · Code for DMEF Customer Lifetime Value ...
Oct 9, 2023 · The comprehensive experiments conducted on two real-world datasets demonstrate the superior capability of FedEOC from both aspects of accuracy ...
Apr 28, 2022 · We develop an algorithm that allows each cluster to communicate independently and derive the convergence results. We study a hierarchical linear ...
Nov 10, 2021 · Experimental results on multiple real-world datasets show that our approach surpasses the state-of-the-art methods on test accuracy by a significant margin. One ...
In this paper, we choose to recursively separate two groups of clients with inconsistent descent directions based on the cosine similarity of parameter ...
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Federated Learning (FL) aims to train a model across multiple parties while preserving the privacy of users' data. Traditional FL only develops a common ...