In healthcare, the development and deployment of insufficiently fair systems of artificial
intelligence (AI) can undermine the delivery of equitable care. Assessments of AI models …

The communication and networking field is hungry for machine learning decision-making
solutions to replace the traditional model-driven approaches that proved to be not rich …

Federated Learning (FL) is an emerging distributed learning paradigm under privacy
constraint. Data heterogeneity is one of the main challenges in FL, which results in slow …

Federated learning has emerged as a promising paradigm for privacy-preserving
collaboration among different parties. Recently, with the popularity of federated learning, an …

The standard objective in machine learning is to train a single model for all users. However,
in many learning scenarios, such as cloud computing and federated learning, it is possible …

Federated learning is promising for its capability to collaboratively train models with multiple
clients without accessing their data, but vulnerable when clients' data distributions diverge …

While federated learning traditionally aims to train a single global model across
decentralized local datasets, one model may not always be ideal for all participating clients …

We propose a new optimization formulation for training federated learning models. The
standard formulation has the form of an empirical risk minimization problem constructed to …

Large-scale labeled training datasets have enabled deep neural networks to excel across a
wide range of benchmark vision tasks. However, in many applications, it is prohibitively …

Federated learning (FL) is a promising approach for training decentralized data located on
local client devices while improving efficiency and privacy. However, the distribution and …