Results 31 to 40 of about 34,864,804 (179)
, 2023 <p>First version of code for benchmarking normalization layers in federated learning for image classification tasks on non-iid data</p ...
Bruno Casella
core +1 more source
Data augmentation scheme for federated learning with non-IID data
Tongxin xuebao, 2023 To solve the problem that the model accuracy remains low when the data are not independent and identically distributed (non-IID) across different clients in federated learning, a privacy-preserving data augmentation scheme was proposed.Firstly, a data ...
Lingtao TANG, Di WANG, Shengyun LIU
doaj +2 more sources
A Graph Neural Network Based Decentralized Learning Scheme
Sensors, 2022 As an emerging paradigm considering data privacy and transmission efficiency, decentralized learning aims to acquire a global model using the training data distributed over many user devices.
Huiguo Gao +3 more
doaj +1 more source
On the Convergence of FedAvg on Non-IID Data
CoRR, 2019 2020 International Conference on Learning ...
Xiang Li 0050 +4 more
openaire +3 more sources
Node Selection Toward Faster Convergence for Federated Learning on Non-IID Data [PDF]
IEEE Transactions on Network Science and Engineering, 2021 Federated Learning (FL) is a distributed learning paradigm that enables a large number of resource-limited nodes to collaboratively train a model without data sharing.
Hong-Da Wu, Ping Wang
semanticscholar +1 more source
Byzantine-robust federated learning over Non-IID data
Tongxin xuebao, 2023 The malicious attacks of Byzantine nodes in federated learning was studied over the non-independent and identically distributed dataset , and a privacy protection robust gradient aggregation algorithm was proposed.A reference gradient was designed to ...
Xindi MA +6 more
doaj +2 more sources
Unsupervised Coupled Metric Similarity for Non-IID Categorical Data [PDF]
, 2018 © 1989-2012 IEEE. Appropriate similarity measures always play a critical role in data analytics, learning, and processing. Measuring the intrinsic similarity of categorical data for unsupervised learning has not been substantially addressed, and even ...
Jian, S +7 more
core +1 more source
Decoupled Federated Learning for ASR with Non-IID Data
Interspeech 2022, 2022 Automatic speech recognition (ASR) with federated learning (FL) makes it possible to leverage data from multiple clients without compromising privacy. The quality of FL-based ASR could be measured by recognition performance, communication and computation costs. When data among different clients are not independently and identically distributed (non-IID)
Han Zhu 0004 +4 more
openaire +3 more sources
Federated XGBoost on Sample-Wise Non-IID Data
CoRR, 2022 Federated Learning (FL) is a paradigm for jointly training machine learning algorithms in a decentralized manner which allows for parties to communicate with an aggregator to create and train a model, without exposing the underlying raw data distribution of the local parties involved in the training process.
Katelinh Jones +3 more
openaire +2 more sources
Beyond i.i.d.: Non-IID Thinking, Informatics, and Learning
, 2023 In science, technology, engineering, and their applications, a ubiquitous assumption is independent and identically distributed (i.i.d. or IID).
Cao, L
core +1 more source