FedGA-Meta: Federated Learning Framework using Genetic Algorithms and Meta-Learning for Aggregation in Industrial Cyber- Physical Systems

Abstract

In Industry 4.0, factories encounter significant challenges in making informed decisions to maintain or enhance their industry standing. By utilizing machine learning (ML), they can improve product quality, decrease production downtime, and boost operational efficiency. However, acquiring datasets with sufficient variation and diversity to train a robust neural network centrally is a challenge within the industrial sector. Consequently, federated learning (FL) offers a decentralized approach that safeguards data privacy, enabling smart infrastructures to train collaborative models locally and independently while retaining local data. In this paper, we present FedGA-Meta framework, which combines FL, meta-learning, and domain adaptation to enhance model performance and generalizability, particularly when training across distributed factories with varying network and data conditions. The results obtained demonstrate the effectiveness and efficiency of our FedGA-Meta framework.