Results 241 to 250 of about 96,144 (271)

On Decentralizing Federated Learning

2020 IEEE International Conference on Systems, Man, and Cybernetics (SMC), 2020
Federated Learning (FL), a distributed version of Deep Learning (DL), was introduced to tackle the problem of user privacy and huge bandwidth requirements in sending the user data to the company servers that run DL models. FL enables on-device training of the models.
Akul Agrawal, Divya D. Kulkarni 0001, Shivashankar B. Nair   +2 more
openaire   +1 more source

Federated learning and privacy

Communications of the ACM, 2022
Building privacy-preserving systems for machine learning and data science on decentralized data.
Kallista A. Bonawitz, Peter Kairouz, Brendan McMahan, Daniel Ramage   +3 more
openaire   +1 more source

Federated Learning

2020
How is it possible to allow multiple data owners to collaboratively train and use a shared prediction model while keeping all the local training data private Traditional machine learning approaches need to combine all data at one location, typically a data center, which may very well violate the laws on user privacy and data confidentiality.
Yang, Qiang ECE, Liu, Yang, Cheng, Yong, Kang, Yan, Chen, Tianjian, Yu, Han   +5 more
openaire   +3 more sources

Federated Regularization Learning: an Accurate and Safe Method for Federated Learning

2021 IEEE 3rd International Conference on Artificial Intelligence Circuits and Systems (AICAS), 2021
Distributed machine learning (ML) and other related techniques such as federated learning are facing a high risk of information leakage. Differential privacy (DP) is commonly used to protect privacy. However, it suffers from low accuracy due to the unbalanced data distribution in federated learning and additional noise brought by DP itself.
Tianqi Su, Meiqi Wang, Zhongfeng Wang 0001   +2 more
openaire   +1 more source

A Survey on federated learning

2020 IEEE 16th International Conference on Control & Automation (ICCA), 2020
Federated learning (FL) is an emerging setting which implement machine learning in a distributed environment while protecting privacy. Research activities relating to FLhave grown at a fast rate recently in control. Exactly what activities have been carrying the research momentum forward is a question of interest to the research community.
Li Li 0008, Yuxi Fan, Kuo-Yi Lin
openaire   +1 more source

Utility-preserving Federated Learning

Proceedings of the 16th ACM Workshop on Artificial Intelligence and Security, 2023
We investigate the concept of utility-preserving federated learning (UPFL) in the context of deep neural networks. We theoretically prove and experimentally validate that UPFL achieves the same accuracy as centralized training independent of the data distribution across the clients.
Reza Nasirigerdeh, Daniel Rueckert, Georgios Kaissis   +2 more
openaire   +2 more sources

Federated Learning of Things - Expanding the Heterogeneity in Federated Learning

Proceedings of the AAAI Symposium Series
The Internet of Things (IoT) has revolutionized how our devices are networked, connecting multiple aspects of our life from smart homes and wearables to smart cities and warehouses. IoT’s strength comes from the ever-expanding diverse heterogeneous sensors, applications, and concepts that are all centered around the core concept collecting and ...
openaire   +1 more source

Federated Learning

Journal of Artificial Intelligence & Cloud Computing, 2022
Federated Learning (FL) serves as one of the groundbreaking approaches in the present society, particularly in smart mobile applications, for designing a distributed environment for clients' model training without compromising data ownership. This paper narrows down the focus to how FL emerged, how it fits in distributed systems, and its usefulness in ...
openaire   +1 more source

Online Federated Learning

2021 60th IEEE Conference on Decision and Control (CDC), 2021
Aritra Mitra, Hamed Hassani, George J. Pappas   +2 more
openaire   +1 more source

Federated Learning for Internet of Things: Recent Advances, Taxonomy, and Open Challenges

IEEE Communications Surveys and Tutorials, 2021
Latif U Khan, Walid Saad, Zhu Han
exaly