Federated learning (FL) is one of the leading paradigms of modern times with higher privacy
guarantees than any other digital solution. Since its inception in 2016, FL has been …

Federated Learning (FL), as a privacy-preserving distributed machine learning paradigm,
has become a promising privacy computing framework for increasingly complex network …

In this paper, we study the problem of privacy-preserving data synthesis (PPDS) for tabular
data in a distributed multi-party environment. In a decentralized setting, for PPDS, federated …

Federated Learning (FL) is a distributed machine learning paradigm where clients
collaboratively train a model using their local (human-generated) datasets. While existing …

Federated learning (FL) is a promising decentralized machine learning approach that
enables multiple distributed clients to train a model jointly while keeping their data private …

Federated learning has gained popularity for distributed learning without aggregating
sensitive data from clients. But meanwhile, the distributed and isolated nature of data …

Federated learning (FL) enables edge devices to cooperatively train models without
exposing their raw data. However, implementing a practical FL system at the network edge …

Federated Learning (FL) is a distributed machine learning paradigm where clients
collaboratively train a model using their local datasets. While existing studies focus on FL …

Federated learning (FL) is considered a de facto standard for privacy preservation in AI
environments because it does not require data to be aggregated in some central place to …

Federated Learning (FL) is a distributed machine learning paradigm that enables learning
models from decentralized private datasets where the labeling effort is entrusted to the …