Results 21 to 30 of about 34,864,804 (179)
FedDC: Federated Learning with Non-IID Data via Local Drift Decoupling and Correction [PDF]
Computer Vision and Pattern Recognition, 2022 Federated learning (FL) allows multiple clients to collectively train a high-performance global model without sharing their private data. However, the key challenge in federated learning is that the clients have significant statistical heterogeneity ...
Liang Gao +5 more
semanticscholar +1 more source
A Multiscale Clustering Approach for Non‐IID Nominal Data [PDF]
Computational Intelligence and Neuroscience, 2021 Multiscale brings great benefits for people to observe objects or problems from different perspectives. Multiscale clustering has been widely studied in various disciplines. However, most of the research studies are only for the numerical dataset, which is a lack of research on the clustering of nominal dataset, especially the data are nonindependent ...
Runzi Chen, Shuliang Zhao, Zhenzhen Tian
openaire +3 more sources
Federated Data Augmentation Algorithm for Non-independent and Identical Distributed Data [PDF]
Jisuanji kexue, 2022 In federated learning,the local data distribution of users changes with the location and preferences of users,the data under the non-independent and identical distributed(Non-IID) data may lack data of some label categories,which significantly affects ...
QU Xiang-mou, WU Ying-bo, JIANG Xiao-ling
doaj +1 more source
Personalized Cross-Silo Federated Learning on Non-IID Data [PDF]
AAAI Conference on Artificial Intelligence, 2020 Non-IID data present a tough challenge for federated learning. In this paper, we explore a novel idea of facilitating pairwise collaborations between clients with similar data. We propose FedAMP, a new method employing federated attentive message passing
Yutao Huang +6 more
semanticscholar +1 more source
Federated learning with hierarchical clustering of local updates to improve training on non-IID data [PDF]
IEEE International Joint Conference on Neural Network, 2020 Federated learning (FL) is a well established method for performing machine learning tasks over massively distributed data. However in settings where data is distributed in a non-iid (not independent and identically distributed) fashion - as is typical ...
Christopher Briggs +2 more
semanticscholar +1 more source
Adaptive Federated Learning With Non-IID Data
The Computer Journal, 2022 Abstract With the widespread use of Internet of things(IoT) devices, it generates an enormous volume of data, and it is a challenge to mine the IoT data value while ensuring security and privacy. Federated learning is a decentralized approach for training data located on edge devices, such as mobile phones and IoT devices, while keeping ...
Yan Zeng +7 more
openaire +1 more source
Peer-to-Peer Learning+Consensus with Non-IID Data
2023 57th Asilomar Conference on Signals, Systems, and Computers, 2023 Peer-to-peer deep learning algorithms are enabling distributed edge devices to collaboratively train deep neural networks without exchanging raw training data or relying on a central server. Peer-to-Peer Learning (P2PL) and other algorithms based on Distributed Local-Update Stochastic/mini-batch Gradient Descent (local DSGD) rely on interleaving epochs
Srinivasa Pranav, José M. F. Moura
openaire +2 more sources
Privacy-Enhanced Federated Learning for Non-IID Data
Mathematics, 2023 Federated learning (FL) allows the collaborative training of a collective model by a vast number of decentralized clients while ensuring that these clients’ data remain private and are not shared. In practical situations, the training data utilized in FL
Qingjie Tan, Shuhui Wu, Yuanhong Tao
doaj +1 more source
Cross-Domain Federated Data Modeling on Non-IID Data
Computational Intelligence and Neuroscience, 2022 Federated learning has received sustained attention in recent years for its distributed training model that fully satisfies the need for privacy concerns. However, under the nonindependent identical distribution, the data heterogeneity of different parties with different data patterns significantly degrades the prediction performance of the federated ...
Baobao Chai, Kun Liu, Ruiping Yang
openaire +2 more sources
, 2023 Personalized federated learning simulation platform with non-IID and unbalanced ...
Tsing +3 more
core +1 more source