Federated learning is a knowledge transmission and training process that occuring in turn between user models at edge devices and the training model at the central server. Due to privacy policies, concerns and heterogeneous data, this is a widespread requirement in federated learning applications. In this work, we use knowledge-based methods and in particular case-based reasoning (CBR) to develop a wearable explainable artificial intelligence (xAI) framework. CBR is a problem-solving AI approach for knowledge representation and manipulation which considers successful solutions of past conditions that are likely to serve as candidate solutions for a requested problem. It enables federated learning when each user owns not only his/her private data, but also uniquely designed cases. New generated cases can be compared to the knowledge base and the recommendations enable the user to communicate better with the whole system. It improves users' task performance and increases user acceptability while they need explanations to understand why and how AI algorithms arrive at these solutions which is the best decision.