In every communication round of federated learning, a random subset of clients
communicate their model updates back to the server which then aggregates them all. The …

Federated Learning (FL) is a distributed machine learning approach in which clients
contribute to learning a global model in a privacy preserved manner. Effective aggregation …

Federated learning (FL) facilitates collaboration between a group of clients who seek to train
a common machine learning model without directly sharing their local data. Although there …

Numerous research recently proposed integrating Federated Learning (FL) to address the
privacy concerns of using machine learning in privacy-sensitive firms. However, the …

Federated Learning allows the training of machine learning models by using the
computation and private data resources of many distributed clients. Most existing results on …

Federated Learning, as a distributed learning technique, has emerged with the improvement
of the performance of IoT and edge devices. The emergence of this learning method alters …

Typical federated learning (FL) problem formulation requires learning a single model
suitable for all parties while prohibiting parties from sharing their data with the aggregator …

Federated Learning (FL) is an approach to conduct machine learning without centralizing
training data in a single place, for reasons of privacy, confidentiality or data volume …

Abstract Federated Learning (FL) and Industry 5.0's convergence holds significant promise
for changing smart systems. FL, a distributed machine learning method, allows for …

Recently, the ever-growing demand for privacy-oriented machine learning has motivated
researchers to develop federated and decentralized learning techniques, allowing individual …