Nowadays, there is increasing demand for mental health monitoring systems to enable disease diagnoses, such as anxiety and depression. However, the privacy concerns for sensitive data impede its wide adoption. To protect data privacy, federated learning (FL) is proposed to enable decentralized ...
artificial intelligence can contribute to the diagnosis and treatment of depression, the traditional centralized machine learning needs to aggregate patient data, and the data privacy of patients with mental illness needs to be strictly confidential, which hinders machine learning algorithms clinical ...
We combine facial expression and speech signals to find out macro expressions and create an emotion index that is monitored to find the mental health of the user. Federated learning enables users to locally train the model without compromising his/her privacy. In place of sending data to the ...
Advances in ML, and particularly deep learning (DL), have shown promise in addressing these complex healthcare problems. However, there are concerns about their generalizability on data from sources that did not participate in model training, i.e., “out-of-sample” data1,2. Literature ...
Besides the joint model, all other five methods were free from data exchange or centralization in the multicenter study setting, thus protecting the privacy of the patient health data. Statistical analysis This study aimed to analyze the effectiveness of federated deep learning on the task of chest...
As a promising paradigm of distributed learning, federated learning has garnered considerable attention since its emergence. However, traditional federated learning solutions based on a central server are not efficient and scalable. Moreover, the centralized design relies on a trustworthy party coordinating...
With the fast development of artificial intelligence (AI) and industrial Internet of Things (IIoT) technologies, it is challenging to deal with the problems of data privacy protection and secure computing. In recent years, federated learning (FL) is prop
3.2. Learning Federated SSF In federated learning, the local data Dk of a client may not be sufficient to train a large scale deep network. Pre- trained models can thus be introduced to compensate for the deficiency of local data [32, 2]. However, pre-trained model us...
As a promising paradigm of distributed learning, federated learning has garnered considerable attention since its emergence. However, traditional federated learning solutions based on a central server are not efficient and scalable. Moreover, the centralized design relies on a trustworthy party coordinating...
Private distributed learning studies the problem of how multiple distributed entities collaboratively train a shared deep network with their private data unrevealed.With the security provided by the protocols of blind quantum computation,the cooperation