The usage of the federated learning (FL) concept in the artificial intelligence (AI) field has
increased. The main concept of FL is to tackle the centralized-based approach, which …

Federated learning is a method for machine learning that avoids the need to move data by
training an algorithm in parallel on a large number of edge devices or servers that hold …

Federated learning (FL) is seen as a road toward privacy-preserving distributed artificial
intelligence while keeping raw training data on local devices. By leveraging blockchain, this …

Federated learning (FL) is an emerging machine learning method in which all participants
can collaboratively train a model without sharing their raw data, thereby breaking down data …

Federated learning is a collaborative machine learning mechanism that allows multiple
parties to develop a model without sharing the training data. It is a promising mechanism …

Currently many enterprises face issues regarding insufficient data collection samples and
data recording dimensions, thus it's hard to make efficient predictions. Since it is limited by …

Blockchain-empowered federated learning (FL) has provoked extensive research recently.
Various blockchain-based federated learning algorithm, architecture and mechanism have …

Blockchain promises to enhance distributed machine learning (ML) approaches such as
federated learning (FL) by providing further decentralization, security, immutability, and trust …

Edge computing network (ECN), which could process learning tasks at the edge, is
considered as a potential solution to release the burden of the cloud. Meanwhile, to protect …

Blockchain and Deep Learning (DL) are two of the most revolutionary concepts in the field of
Computer Science. Both have made astounding leaps in research and application areas …