Full leverage of the huge volume of data generated on a large number of user devices for
providing intelligent services in the 6G network calls for Ubiquitous Intelligence (UI). A key to …

Federated learning (FL) is a machine learning technique that allows for on-site data
collection and processing without sacrificing data privacy and transmission. Heterogeneity is …

Pneumonia is a significant global health concern that can lead to severe and sometimes
fatal consequences. Timely identification and classification of pneumonia can substantially …

Federated learning has emerged as a promising approach for collaborative model training
across distributed devices. Federated learning faces challenges such as Non-Independent …

Federated learning (FL) is an emerging distributed machine learning paradigm that enables
collaborative training of machine learning models over decentralized devices without …

Federated learning is a promising paradigm for applications across a variety of domains.
However, there are some challenges that must be addressed in real-world scenarios …

Consumer Internet of Things (CIoT) interconnects multiple devices over internet, like
smartphones, wearables, and smart gadgets to simplify tasks and provide convenience …

Federated fault diagnosis has drawn increased attention recently, which makes use of
datasets from different clients with data privacy. However, data distribution varies across …

Data heterogeneity may significantly deteriorate the performance of federated learning since
the client's data distribution is divergent. To mitigate this issue, an effective method is to …

Federated learning is a potential solution for training secure machine learning models on a
decentralized network of clients, with an emphasis on privacy. However, the management of …