Federated learning is a distributed learning approach that balances data privacy and
collaborative learning. To address the impact of non-IID (non-Independently and Identically …

Federated learning (FL) was proposed for training a deep neural network model using
millions of user data. The technique has attracted considerable attention owing to its privacy …

Federated Learning has been widely used due to its ability to train models while ensuring
data privacy and security. However, the presence of non-iid (independent and identically …

With billions of IoT devices producing vast data globally, privacy and efficiency challenges
arise in AI applications. Federated learning (FL) has been widely adopted to train deep …

Federated learning is a new machine learning paradigm in which multiple clients
collaborate to train a machine learning model while protecting local data privacy. Client-side …

Federated Learning (FL) has emerged as a new paradigm for training machine learning
models distributively without sacrificing data security and privacy. Learning models on edge …

Federated learning is a novel distributed machine learning framework based on data privacy
protection. In practical applications, there are often significant differences in data distribution …

Federated learning (FL) enables resource-constrained edge nodes to collaboratively learn a
global model under the orchestration of a central server while keeping privacy-sensitive data …

Federated learning is a promising machine learning paradigm that enables participating
clients to train models collaboratively with privacy restrictions. However, one of the most …

Federated learning is widely accepted as a privacy-preserving paradigm for training a
shared global model across multiple client devices in a collaborative fashion. However, in …