Results 21 to 30 of about 91,570 (234)
Hybrid Federated and Centralized Learning [PDF]
2021 29th European Signal Processing Conference (EUSIPCO), 2021 Many of the machine learning (ML) tasks are focused on centralized learning (CL), which requires the transmission of local datasets from the clients to a parameter server (PS) leading to a huge communication overhead. Federated learning (FL) overcomes this issue by allowing the clients to send only the model updates to the PS instead of the whole ...
Elbir, Ahmet M. +2 more
openaire +3 more sources
A federated learning algorithm using parallel-ensemble method on non-IID datasets
Complex & Intelligent Systems, 2023 Traditional federated learning algorithms suffer from considerable performance reduction with non-identically and independently distributed datasets. This paper proposes a federated learning algorithm based on parallel-ensemble learning, which improves ...
Haoran Yu +5 more
doaj +1 more source
Federated Quantum Machine Learning
Entropy, 2021 Distributed training across several quantum computers could significantly improve the training time and if we could share the learned model, not the data, it could potentially improve the data privacy as the training would happen where the data is located.
Samuel Yen-Chi Chen, Shinjae Yoo
openaire +4 more sources
Iraqi Journal for Computer Science and Mathematics, 2023 This work presents a novel method for securing federated learning in healthcare applications, focusing on skin cancer classification. The suggested solution detects and mitigates data poisoning attacks using deep learning and CNN architecture ...
Alaa Hamza Omran +2 more
doaj +1 more source
Privatized Graph Federated Learning
EURASIP Journal on Advances in Signal Processing, 2023 Abstract Federated learning is a semi-distributed algorithm, where a server communicates with multiple dispersed clients to learn a global model. The federated architecture is not robust and is sensitive to communication and computational overloads due to its one-master multi-client structure. It can also be subject to privacy attacks targeting
Elsa Rizk, Stefan Vlaski, Ali H. Sayed
openaire +4 more sources
Hierarchical Federated Learning Algorithm Based on EMD Optimal Matching [PDF]
Jisuanji gongchengFederated learning allows multiple clients to cooperatively train a high-performance global model without sharing private data. In a horizontal federated learning environment involving cross-silo scenarios, the statistical heterogeneity in the ...
WU Xiaohong, LI Pei, GU Yonggen, TAO Jie
doaj +1 more source
JMIR mHealth and uHealth, 2021 BackgroundThe use of wearables facilitates data collection at a previously unobtainable scale, enabling the construction of complex predictive models with the potential to improve health.
Liu, Jessica Chia +3 more
doaj +1 more source
Federated learning in food research
Journal of Agriculture and Food ResearchThe use of machine learning in food research is sometimes limited due to data sharing obstacles such as data ownership and privacy requirements. Federated learning is a technique to potentially alleviate these obstacles because it allows to train machine
Zuzanna Fendor +5 more
doaj +1 more source
A Hybrid Approach to Privacy-Preserving Federated Learning
, 2019 Federated learning facilitates the collaborative training of models without the sharing of raw data. However, recent attacks demonstrate that simply maintaining data locality during training processes does not provide sufficient privacy guarantees ...
Anwar, Ali +6 more
core +1 more source
Turbo-Aggregate: Breaking the Quadratic Aggregation Barrier in Secure Federated Learning [PDF]
, 2020 Federated learning is a distributed framework for training machine learning models over the data residing at mobile devices, while protecting the privacy of individual users.
Avestimehr, A. Salman +2 more
core +1 more source