Results 21 to 30 of about 17,541 (167)

On a Framework for Federated Cluster Analysis

Applied Sciences, 2022
Federated learning is becoming increasingly popular to enable automated learning in distributed networks of autonomous partners without sharing raw data.
Morris Stallmann, Anna Wilbik
doaj   +1 more source

A Trusted Federated Incentive Mechanism Based on Blockchain for 6G Network Data Security

Applied Sciences, 2023
The machine learning paradigms driven by the sixth-generation network (6G) facilitate an ultra-fast and low-latency communication environment. However, specific research and practical applications have revealed that there are still various issues ...
Yihang Luo, Bei Gong, Haotian Zhu, Chong Guo   +3 more
doaj   +1 more source

Blind Federated Edge Learning [PDF]

IEEE Transactions on Wireless Communications, 2021
submitted for publication.
Mohammad Mohammadi Amiri, Tolga M. Duman, Deniz Gündüz, Sanjeev R. Kulkarni, H. Vincent Poor   +4 more
openaire   +4 more sources

Modular Federated Learning

2022 International Joint Conference on Neural Networks (IJCNN), 2022
To be published in IEEE IJCNN 2022 ...
Kuo-Yun Liang, Abhishek Srinivasan, Juan Carlos Andresen   +2 more
openaire   +2 more sources

Federated Learning for Thyroid Ultrasound Image Analysis to Protect Personal Information: Validation Study in a Real Health Care Environment

JMIR Medical Informatics, 2021
BackgroundFederated learning is a decentralized approach to machine learning; it is a training strategy that overcomes medical data privacy regulations and generalizes deep learning algorithms.
Lee, Haeyun, Chai, Young Jun, Joo, Hyunjin, Lee, Kyungsu, Hwang, Jae Youn, Kim, Seok-Mo, Kim, Kwangsoon, Nam, Inn-Chul, Choi, June Young, Yu, Hyeong Won, Lee, Myung-Chul, Masuoka, Hiroo, Miyauchi, Akira, Lee, Kyu Eun, Kim, Sungwan, Kong, Hyoun-Joong   +15 more
doaj   +1 more source

Coded Federated Learning [PDF]

2019 IEEE Globecom Workshops (GC Wkshps), 2019
Federated learning is a method of training a global model from decentralized data distributed across client devices. Here, model parameters are computed locally by each client device and exchanged with a central server, which aggregates the local models for a global view, without requiring sharing of training data.
Sagar Dhakal, Saurav Prakash, Yair Yona, Shilpa Talwar, Nageen Himayat   +4 more
openaire   +2 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, Chang Wu, Haixin Yu, Xuelin Wei, Siyan Liu, Ying Zhang   +5 more
doaj   +1 more source

Agnostic Federated Learning

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, Gary Sivek, Ananda Theertha Suresh   +2 more
openaire   +3 more sources

Federated Residual Learning

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, John Langford 0001, Chen-Yu Wei   +2 more
openaire   +2 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