This paper presents a fully coupled blockchain-assisted federated learning architecture that
effectively eliminates single points of failure by decentralizing both the training and …

Many researchers have proposed replacing the aggregation server in federated learning
with a blockchain system to improve privacy, robustness, and scalability. In this approach …

Blockchain based federated learning is a distributed learning scheme that allows model
training without participants sharing their local data sets, where the blockchain components …

Federated Learning is a decentralized framework that enables multiple clients to
collaboratively train a machine learning model under the orchestration of a central server …

Federated Learning harnesses data from multiple sources to build a single model. While the
initial model might belong solely to the actor bringing it to the network for training …

Federated learning (FL) has received considerable attention because it allows multiple
devices to train models locally without revealing sensitive data. Well-trained local models …

With the increasing importance of machine learning, the privacy and security of training data
have become critical. Federated learning, which stores data in distributed nodes and shares …

Federated learning (FL) is a promising distributed learning solution that only exchanges
model parameters without revealing raw data. However, the centralized architecture of FL is …

In recent years, machine learning models have evolved, and the training of these models
requires large amounts of data. However, the training data often contains sensitive …

Federated learning (FL) is emerging as a sought-after distributed machine learning
architecture, offering the advantage of model training without direct exposure of raw data …