Recent advances in machine learning have highlighted Federated Learning (FL) as a
promising approach that enables multiple distributed users (so-called clients) to collectively …

Federated learning (FL) has emerged as a secure paradigm for collaborative training among
clients. Without data centralization, FL allows clients to share local information in a privacy …

Federated learning allows multiple clients to collaboratively train a model without
exchanging their data, thus preserving data privacy. Unfortunately, it suffers significant …

Traditional Federated Learning (FL) follows a server-dominated cooperation paradigm
which narrows the application scenarios of FL and decreases the enthusiasm of data …

Federated learning (FL) has emerged as a privacy-preserving paradigm that trains neural
networks on edge devices without collecting data at a central server. However, FL …

Federated learning (FL) is widely used in edge environments as a privacy-preserving
collaborative learning paradigm. However, edge devices often have heterogeneous …

Federated learning (FL) is a distributed learning paradigm that maximizes the potential of
data-driven models for edge devices without sharing their raw data. However, devices often …

Synthetic data generation plays a crucial role in many areas where data is scarce and
privacy/confidentiality is a significant concern. Generative Adversarial Networks (GANs) …

With the rapid growth of the number of devices generating and collecting data, dispersion
becomes an important feature of data in Internet of Things. Federated learning (FL) provides …

Federated learning (FL) enables large amounts of participants to construct a global learning
model, while storing training data privately at local client devices. A fundamental issue in FL …