The Industrial Internet of Things (IIoT) provides an unprecedented opportunity to address the issue on a macro scale. However, traditional centralized learning approaches cannot manage the enormous amount of data from anywhere, like IIoE devicesCreateTime. Therefore, federated learning began to be considered as a solution for decentralized collaborative model training that will maintain the confidentiality of data. This chapter investigates the effectiveness of federated learning on energy-efficient optimization within IIoE and provides insight into different federated learning algorithms and architectures for energy-efficient operations, exploring the resource-constrained characteristics of IIoE devices. Additionally, the chapter discusses how federated learning would benefit intelligent energy management systems, predictive maintenance, and resource configuration.