Recently, blockchain-based federated learning (BFL) has attracted intensive research
attention due to that the training process is auditable and the architecture is serverless …

Blockchain technology has been extensively studied to enable distributed and tamper-proof
data processing in federated learning (FL). Most existing blockchain assisted FL (BFL) …

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 a decentralized learning method that deviated from the
conventional centralized learning. The FL progresses learning locally on each device and …

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

Federated learning is an emerging paradigm to conduct the machine learning
collaboratively but avoid the leakage of original data. Then, how to motivate the data owners …

The recently emerging federated learning (FL) exploits massive data stored at multiple user
nodes to train a global optimal learning model without leaking the privacy of user data …

Blockchain-based federated learning (BCFL) has recently gained tremendous attention
because of its advantages, such as decentralization and privacy protection of raw data …

Despite the advantages of Federated Learning (FL), such as devolving model training to
intelligent devices and preserving data privacy, FL still faces the risk of the single point of …

Federated Learning (FL) is a promising solution for training using data collected from
heterogeneous sources (eg, mobile devices) while avoiding the transmission of large …