Results 21 to 30 of about 17,879 (167)

Green Federated Learning

CoRR, 2023
The rapid progress of AI is fueled by increasingly large and computationally intensive machine learning models and datasets. As a consequence, the amount of compute used in training state-of-the-art models is exponentially increasing (doubling every 10 months between 2015 and 2022), resulting in a large carbon footprint.
Ashkan Yousefpour, Shen Guo, Ashish Shenoy, Sayan Ghosh, Pierre Stock, Kiwan Maeng, Schalk-Willem Krüger, Michael G. Rabbat, Carole-Jean Wu, Ilya Mironov   +9 more
openaire   +2 more sources

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

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

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

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

Active Federated Learning

CoRR, 2019
Federated Learning allows for population level models to be trained without centralizing client data by transmitting the global model to clients, calculating gradients locally, then averaging the gradients. Downloading models and uploading gradients uses the client's bandwidth, so minimizing these transmission costs is important.
Jack Goetz, Kshitiz Malik, Duc Bui, Seungwhan Moon, Honglei Liu, Anuj Kumar   +5 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

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

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

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