Results 31 to 40 of about 1,704,441 (287)
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
Ensemble Federated Adversarial Training with Non-IID data
CoRR, 2021 Despite federated learning endows distributed clients with a cooperative training mode under the premise of protecting data privacy and security, the clients are still vulnerable when encountering adversarial samples due to the lack of robustness.
Shuang Luo +3 more
openaire +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
Federated proximal learning with data augmentation for brain tumor classification under heterogeneous data distributions [PDF]
PeerJ Computer ScienceThe increasing use of electronic health records (EHRs) has transformed healthcare management, yet data sharing across institutions remains limited due to privacy concerns.
Swetha Ghanta +5 more
doaj +2 more sources
Engineering Proceedings, 2023 Federated learning (FL) is a field in distributed optimization. Therein, the collection of data and training of neural networks (NN) are decentralized, meaning that these tasks are carried out across multiple clients with limited communication and ...
Tobias Sukianto +4 more
doaj +1 more source
Performance gap between IID and non-IID data.
, 2020 Performance gap between IID and non-IID data.
Zeng Fu (8727135) +5 more
core +1 more source
IEEE Access, 2023 Data scientists in the Natural Language Processing (NLP) field confront the challenge of reconciling the necessity for data-centric analyses with the imperative to safeguard sensitive information, all while managing the substantial costs linked to the ...
Pascal Riedel +5 more
doaj +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 +2 more sources
FedRDS: Federated Learning on Non-IID Data via Regularization and Data Sharing
Applied Sciences, 2023 Federated learning (FL) is an emerging decentralized machine learning framework enabling private global model training by collaboratively leveraging local client data without transferring it centrally.
Yankai Lv +4 more
doaj +1 more source
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