scholar.google.com › citations
The end results of the study indicate a positive relationship between all the variables. Recommendations in the present study have been made to enhance ...
Nov 3, 2023 · Non-IID Federated Learning Based on Global Knowledge Sharing Without Out-of-Domain Data. Yufei Zhang 1. ,. Tianxing Xu 1.
People also ask
What is non-iid data in federated learning?
Is non-iid data a threat in federated online learning to rank?
What is non-iid data distribution?
What are the limitations of federated learning?
May 1, 2024 · Federated learning in medicine: facilitating multi-institutional collaborations without sharing patient data. Scientific reports, 10(1): ...
Federated learning enables multiple decentralized clients to learn collaboratively without sharing local data. However, the expensive annotation cost on ...
These heterogeneous samples are usually known as non-IID data [5], and it is one of the main difficulties encountered in the federated learning process.
Nov 16, 2023 · In this paper, we design a novel FL framework based on deep reinforcement learning (DRL), named FedRLCS.
Apr 20, 2024 · FL operates by aggregating models trained by remote devices which owns the data. Thus, FL enables the training of powerful global models using ...
A core challenge in federated systems is the non-IID problem, which also widely exists in real-world graph data. For example, local data of clients may come ...
Federated Learning of Non-IID Data. An early proposition to handle non-IID data in federated learning was to create a globally shared dataset comprising a ...
This paper utilizes the overlapping clustering algorithm with linear time complexity to addresses inherent non-IID challenges in federated learning.
Missing: Knowledge | Show results with:Knowledge