Existing thermal comfort prediction approaches by machine learning models have been achieving great success based on large datasets in sustainable Industry 4.0 environment. However, the industrial Internet of Things (IoT) environment generates small-scale datasets where each dataset may contain lots of worker’s private data. The latter is challenging the current prediction approaches as small datasets running a large number of iterations can result in overfitting. Moreover, worker’s privacy has been a public concern throughout recent years. Therefore, there must be a trade-off between developing accurate thermal comfort prediction models and worker’s privacy-preserving. To tackle this challenge, we present a privacy-preserving machine learning technique, federated learning (FL), where an FL-based neural network algorithm (Fed-NN) is proposed for thermal comfort prediction. Fed-NN departs from current centralized machine learning approaches where a universal learning model is updated through a secured parameter aggregation process in place of sharing raw data among different industrial IoT environments. Besides, we designed a branch selection protocol to solve the problem of communication overhead in federating learning. Experimental studies on a real dataset reveal the robustness, accuracy, and stability of our algorithm in comparison to other machine learning algorithms while taking privacy into consideration.