Machine learning (ML) is increasingly being adopted in a wide variety of application
domains. Usually, a well-performing ML model relies on a large volume of training data and …

Over the recent years, with the increasing adoption of Federated Learning (FL) algorithms
and growing concerns over personal data privacy, Privacy-Preserving Federated Learning …

Secure aggregation is a cryptographic protocol that securely computes the aggregation of its
inputs. It is pivotal in keeping model updates private in federated learning. Indeed, the use of …

Privacy and security-related concerns are growing as machine learning reaches diverse
application domains. The data holders want to train or infer with private data while exploiting …

Federated learning (FL) has demonstrated tremendous success in various mission-critical
large-scale scenarios. However, such promising distributed learning paradigm is still …

Federated Learning (FL) enhances decentralized machine learning by safeguarding data
privacy, reducing communication costs, and improving model performance with diverse data …

Federated Learning (FL) is a promising distributed learning approach that enables multiple
clients to collaboratively train a shared global model. However, recent studies show that FL …

Speech emotion recognition (SER) processes speech signals to detect and characterize
expressed perceived emotions. Many SER application systems often acquire and transmit …

Federated learning (FL) has emerged as a promising solution to address the challenges
posed by data silos and the need for global data fusion. It offers a distributed machine …

Federated learning (FL) is a distributed learning process that uses a trusted aggregation
server to allow multiple parties (or clients) to collaboratively train a machine learning model …