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
Federated Geometric Monte Carlo Clustering to Counter Non-IID Datasets [PDF]
, 2022 Federated learning allows clients to collaboratively train models on datasets that are acquired in different locations and that cannot be exchanged because of their size or regulations.
Marcus, Völp +4 more
core +1 more source
Agronomy, 2023 Paddy leaf diseases encompass a range of ailments affecting rice plants’ leaves, arising from factors like bacteria, fungi, viruses, and environmental stress.
Meenakshi Aggarwal +6 more
doaj +1 more source
Federated Learning Architecture for Non-IID Data [PDF]
Jisuanji gongcheng, 2023 In the scenarios of federated learning involving ultra-large-scale edge devices, the local data of participants are non-Independent Identically Distribution(non-IID) pattern, resulting in an imbalance in overall training data and difficulty in defending ...
Tianchen QIU, Xiaoying ZHENG, Yongxin ZHU, Songlin FENG
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
Entropy to Mitigate Non-IID Data Problem on Federated Learning for the Edge Intelligence Environment
IEEE Access, 2023 Machine Learning (ML) algorithms process input data making it possible to recognize and extract patterns from a large data volume. Likewise, Internet of Things (IoT) devices provide knowledge in a Federated Learning (FL) environment, sharing parameters ...
Fernanda C. Orlandi +4 more
doaj +1 more source
Non-IID Quantum Federated Learning with One-shot Communication Complexity [PDF]
, 2023 Federated learning refers to the task of machine learning based on decentralized data from multiple clients with secured data privacy. Recent studies show that quantum algorithms can be exploited to boost its performance.
Zhao, Haimeng, Haimeng Zhao
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
The infectious intestinal disease study of England: a prospective evaluation of symptoms and health care use after an acute episode [PDF]
, 2003 The sequelae of Infectious Intestinal Disease (IID) in a population-based sample of cases and matched controls were investigated for a period of 3 months following the initial infection.
Rodrigues, L.C. +18 more
core +1 more source
Enhancing Federated Learning robustness through non-IID features [PDF]
, 2022 Federated Learning (FL) enables many clients to train a joint model without sharing the raw data. While many byzantine-robust FL methods have been proposed, FL remains vulnerable to security attacks (such as poisoning attacks and evasion attacks) because
Yuan, Dong +3 more
core +1 more source