SortAlign: a score-based aggregation technique for neural networks in one-round federated learning

@Article {
title = {SortAlign: a score-based aggregation technique for neural networks in one-round federated learning},
journal = {Systems Science and Control Engieering},
pages = {2421476},
volume = {12},
keywds = {
deep learning; Federated learning; machining process; one-round federated learning
}
abstract = {

In recent years, the growth of data generated on a daily basis in critical domains, such as industrial processes, where data privacy plays a key role, has led to the strong development of Federated Learning. In turn, the need for communication-efficient approaches has given particular importance to One-Round Federated Learning, where a central server coordinates the learning process of a global model using a federated network of clients, or nodes, in a single round of communication. In this study, a novel alignment strategy based on nodes similarity matching for Neural Networks in One-Round Federated Learning is proposed. This method was compared with various federated models and validated using a real-world use case of machining process.

}
doi = {10.1080/21642583.2024.2421476},
date = {2024-01-01},
}