Federated learning (FL) is a distributed machine learning paradigm that needs collaboration
between a server and a series of clients with decentralized data. To make FL effective in real …

Federated learning facilitates collaborative learning among multiple parties while ensuring
client privacy. The vulnerability of federated learning to diverse Byzantine attacks stems from …

Federated learning achieves effective performance in modeling decentralized data. In
practice client data are not well-labeled which makes it potential for federated unsupervised …

Environmental sound classification (ESC) has received intensive attention, especially with
wide applications in Internet of Things (IoT) field. Recently, federated learning (FL) has been …

Federated Semi-Supervised Learning (FSSL) leverages both labeled and unlabeled data on
clients to collaboratively train a model. In FSSL, the heterogeneous data can introduce …