Clinical trials and drug discovery would not be effective without the collaboration of
institutions. Earlier, it has been at the cost of individual's privacy. Several pacts and …

Federated learning is a promising distributed machine learning paradigm that has been
playing a significant role in providing privacy-preserving learning solutions. However …

Federated learning is a promising tool in the Internet-of-Things (IoT) domain for training a
machine learning model in a decentralized manner. Specifically, the data owners (eg, IoT …

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

Federated learning (FL) is an emerging collaborative machine learning method to train
models on distributed datasets with privacy concerns. To properly incentivize data owners to …

Federated Learning is a promising machine learning paradigm when multiple parties
collaborate to build a high-quality machine learning model. Nonetheless, these parties are …

Federated learning enables clients to train a machine learning model jointly without sharing
their local data. However, due to the centrality of federated learning framework and the …

Decentralized learning involves training machine learning models over remote mobile
devices, edge servers, or cloud servers while keeping data localized. Even though many …

Federated learning (FL), arising as a privacy-preserving machine learning paradigm, has
received notable attention from the public. In each round of synchronous FL training, only a …

Decentralized federated learning (DFL) is an emerging privacy-preserving machine learning
framework, where multiple data owners cooperate to train a global model without any …