Results 1 to 10 of about 17,541 (167)
Energy and AIFederated learning has recently emerged as a privacy-preserving distributed machine learning approach. Federated learning enables collaborative training of multiple clients and entire fleets without sharing the involved training datasets.
Albin Grataloup +2 more
doaj +3 more sources
Survey of Graph Neural Network [PDF]
Jisuanji gongcheng, 2021 With the continuous development of the computer and Internet technologies,graph neural network has become an important research area in artificial intelligence and big data.Graph neural network can effectively transmit and aggregate information between ...
WANG Jianzong, KONG Lingwei, HUANG Zhangcheng, XIAO Jing
doaj +1 more source
Vulnerabilities in Federated Learning [PDF]
IEEE Access, 2021 With more regulations tackling the protection of users’ privacy-sensitive data in recent years, access to such data has become increasingly restricted. A new decentralized training paradigm, known as Federated Learning (FL), enables multiple clients located at different geographical locations to learn a machine learning model collaboratively ...
Nader Bouacida, Prasant Mohapatra
openaire +2 more sources
Efficient Federated Learning Scheme Based on Game Theory Optimization [PDF]
Jisuanji gongcheng, 2022 With the continuous development of network information technology and Internet technology, data privacy and security issues need to be addressed urgently.Federated learning has emerged as a new distributed privacy protection machine learning framework ...
ZHOU Quanxing, LI Qiuxian, DING Hongfa, FAN Meimei
doaj +1 more source
Learning to Backdoor Federated Learning
CoRR, 2023 In a federated learning (FL) system, malicious participants can easily embed backdoors into the aggregated model while maintaining the model's performance on the main task. To this end, various defenses, including training stage aggregation-based defenses and post-training mitigation defenses, have been proposed recently.
Henger Li +3 more
openaire +2 more sources
A Survey of Federated Evaluation in Federated Learning
Proceedings of the Thirty-Second International Joint Conference on Artificial Intelligence, 2023 In traditional machine learning, it is trivial to conduct model evaluation since all data samples are managed centrally by a server. However, model evaluation becomes a challenging problem in federated learning (FL), which is called federated evaluation in this work.
Behnaz Soltani +3 more
openaire +2 more sources
Dynamic Federated Learning [PDF]
2020 IEEE 21st International Workshop on Signal Processing Advances in Wireless Communications (SPAWC), 2020 Federated learning has emerged as an umbrella term for centralized coordination strategies in multi-agent environments. While many federated learning architectures process data in an online manner, and are hence adaptive by nature, most performance analyses assume static optimization problems and offer no guarantees in the presence of drifts in the ...
Elsa Rizk, Stefan Vlaski, Ali H. Sayed
openaire +2 more sources
Fairness-Based Multi-AP Coordination Using Federated Learning in Wi-Fi 7
Sensors, 2022 Federated learning is a type of distributed machine learning in which models learn by using large-scale decentralized data between servers and devices. In a short-range wireless communication environment, it can be difficult to apply federated learning ...
Gimoon Woo +4 more
doaj +1 more source
Incentivizing Federated Learning
CoRR, 2022 Federated Learning is an emerging distributed collaborative learning paradigm used by many of applications nowadays. The effectiveness of federated learning relies on clients' collective efforts and their willingness to contribute local data. However, due to privacy concerns and the costs of data collection and model training, clients may not always ...
Shuyu Kong, You Li 0008, Hai Zhou 0001
openaire +2 more sources
IEEE Access, 2023 Federated learning is a promising approach for training machine learning models using distributed data from multiple mobile devices. However, privacy concerns arise when sensitive data are used for training.
Kijung Jung +3 more
doaj +1 more source