Results 241 to 250 of about 31,221,148 (284)

BCAFL: a secure and efficient blockchain framework for asynchronous federated learning. [PDF]

Sci Rep
Yun J, Liu T.
europepmc   +1 more source

The Random Power Function for Tests Based on Pivotal Quantities

International Statistical Review, EarlyView.
Summary In clinical trials planning, evaluation of the probability of success of an experiment is of central interest, for instance, in sample size determination. This assessment typically involves analyses of the power function of a test on a parameter of interest, such as a relevant treatment effect.
Fulvio De Santis, Stefania Gubbiotti, Francesco Mariani   +2 more
wiley   +1 more source

Communication-Efficient Federated Learning with Dual-Sided Sparse Aggregation for Edge Sensing Systems. [PDF]

Sensors (Basel)
Zhao H, Li J.
europepmc   +1 more source

Experimentally Validated Quantum-Secure Federated Learning over a Multi-user Quantum Network. [PDF]

Research (Wash D C)
Liu ZP, Cao XY, Liu HW, Sun XR, Bao Y, Shen JY, Lu YS, Yin HL, Chen ZB.   +8 more
europepmc   +1 more source

A novel intrusion detection system for IIoT in 5G networks using attention-augmented federated learning and lightweight transformer architectures. [PDF]

Sci Rep
Du J.
europepmc   +1 more source

FL-TWIN: a unified federated learning system for intrusion detection with digital twins modelling. [PDF]

Sci Rep
Hamwi AA, Mittal M.
europepmc   +1 more source

Maternal empowerment and child mortality in the Democratic Republic of the Congo: evidence from multilevel modelling of DHS data. [PDF]

BMC Public Health
Mulamba KC.
europepmc   +1 more source

Federated Learning for non-IID Healthcare Data

International Research Journal of Modernization in Engineering Technology & Science
openaire   +1 more source

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Federated Learning With Non-IID Data in Wireless Networks

IEEE Transactions on Wireless Communications, 2022
Federated learning provides a promising paradigm to enable network edge intelligence in the future sixth generation (6G) systems. However, due to the high dynamics of wireless circumstances and user behavior, the collected training data is non-independent and identically distributed (non-IID), which causes severe performance degradation of federated ...
Tony Q. S. Quek, Mugen Peng, Zhongyuan Zhao   +2 more
exaly   +3 more sources