Results 31 to 40 of about 96,144 (271)
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
Servicing the federation : the case for metadata harvesting [PDF]
, 2004 The paper presents a comparative analysis of data harvesting and distributed computing as complementary models of service delivery within large-scale federated digital libraries.
Agosti, Maristella +3 more
core +6 more sources
Federated Learning With Multichannel ALOHA [PDF]
IEEE Wireless Communications Letters, 2020 4 pages, 4 figures, IEEE WCL (accepted)
Jinho Choi 0001, Shiva Raj Pokhrel
openaire +2 more sources
CoRR, 2020 We study a new form of federated learning where the clients train personalized local models and make predictions jointly with the server-side shared model. Using this new federated learning framework, the complexity of the central shared model can be minimized while still gaining all the performance benefits that joint training provides.
Alekh Agarwal +2 more
openaire +2 more sources
CoRR, 2019 A key learning scenario in large-scale applications is that of federated learning, where a centralized model is trained based on data originating from a large number of clients. We argue that, with the existing training and inference, federated models can be biased towards different clients.
Mehryar Mohri +2 more
openaire +3 more sources
2020 IEEE Wireless Communications and Networking Conference (WCNC), 2020 Federated learning (FL) enables massive distributed Information and Communication Technology (ICT) devices to learn a global consensus model without any participants revealing their own data to the central server. However, the practicality, communication expense and non-independent and identical distribution (Non-IID) data challenges in FL still need ...
Zhikun Chen +4 more
openaire +2 more sources
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
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
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