IoT, EHR, and blockchain, but no work has integrated mental health aspects, which are essential these days. Therefore, this study aims to fill the gap in reviewing the research on federated learning in mental health applications
Federated learning has gotten some attention. This research reviews the studies on federated learning and mental health related to solving the issue of intelligent healthcare systems. It explores various dimensions of federated learning in mental health, such as datasets (their types and sources), ...
Machine Learning (ML) algorithms are generally designed for scenarios in which all data is stored in one data center, where the training is performed. However, in many applications, e.g., in the healthcare domain, the training data is distributed among several entities, e.g., different hospi...
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 ...
Federated Learning (FL), Artificial Intelligence (AI), and Explainable Artificial Intelligence (XAI) are the most trending and exciting technology in the intelligent healthcare field. Traditionally, the healthcare system works based on centralized agents sharing their raw data. Therefore, huge vulnerabil...
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...
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 usu...
LLMs can also improve FL systems by leveraging their task generalization capabilities to address challenges in distributed learning environments. The integration of these two technologies has the potential to transform industries that rely heavily on sensitive data, such as healthcare, finance, and ...
Graph neural network (GNN) is effective in modeling high-order interactions and has been widely used in various personalized applications such as recommendation. However, mainstream personalization methods rely on centralized GNN learning on global graphs, which have considerable privacy risks due to the...
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 ...