Abstract
The rapid advancements in Industry 5.0 necessitate efficient
communication optimization techniques to facilitate seamless collaboration between humans and advanced technologies. This book chapter explores the potential of Federated Learning (FL) as a solution to address
the challenges faced by current communication optimization techniques
in Industry 5.0. FL enables collaborative model training while preserving
data privacy by keeping sensitive data decentralized. By leveraging FL,
organizations can achieve data efficiency by training models locally on
devices or edge servers, reducing the need for extensive data transfer. FL
promotes collaboration and knowledge sharing without sharing raw data,
fostering collective intelligence. Moreover, FL operates in a decentralized
manner, reducing infrastructure costs and enabling efficient communication in distributed environments. The continuous learning capabilities of
FL ensure that models stay up-to-date with changing circumstances. By
minimizing communication overhead through the sharing of encrypted
model parameters or gradients, FL enhances communication efficiency
in Industry 5.0. However, challenges related to managing model updates
and ensuring fairness should be carefully addressed. This book chapter
presents FL as a promising approach to optimize communication in Industry 5.0, offering privacy preservation, data efficiency, collaboration,
decentralized infrastructure, and continuous learning.