Federated Learning (FL) is a machine learning technique in which collaborative and
distributed learning is performed, while the private data reside locally on the client. Rather …

Federated learning enables participants to collaborate on model training without directly
exchanging raw data. Existing federated learning methods often follow the parameter server …

With the rapid development of machine learning and growing concerns about data privacy,
federated learning has become an increasingly prominent focus. However, challenges such …

Federated Learning (FL) has emerged as an effective learning paradigm for distributed
computation owing to its strong potential in capturing underlying data statistics while …

Federated learning (FL), as a distributed machine learning paradigm, promotes personal
privacy by local data processing at each client. However, relying on a centralized server for …

Federated Learning (FL) is an emerging technique for collaboratively training machine
learning models on distributed data under privacy constraints. However, recent studies have …

Federated Learning (FL) enables multiple parties to distributively train a ML model without
revealing their private datasets. However, it assumes trust in the centralized aggregator …

Federated learning has been widely studied and applied to various scenarios, such as
financial credit, medical identification, and so on. Under these settings, federated learning …

Federated learning (FL) has emerged as a promising privacy-preserving distributed
machine learning framework recently. It aims at collaboratively learning a shared global …

Federated learning enables the collaborative training of machine learning models across
multiple organizations, eliminating the need for sharing sensitive data. Nevertheless, in …