Federated Learning (FL) has recently attracted considerable attention in multi-robot collaborative systems, owning to its capability of enabling mobile clients to collaboratively learn a global prediction model without sharing their privacy-sensitive data to the server. In a multi-robot collaboration system, an approach that ensures privacy-preserving knowledge sharing among multiple robots becomes imperative. However, the application of FL in such systems encounters two major challenges. Firstly, it is inefficient to use all the network nodes as federated learning clients (which conduct training of machine learning model based on own data) due to the limited wireless bandwidth and energy of robots. Secondly, the selection of an appropriate number of clients must be carefully considered, considering the constraints imposed by limited communication resources. Selecting an excessive number of clients may result in a failure in uploading important models. To overcome these challenges, this paper proposes a client selection approach that considers multiple metrics including the data volume, computational capability, and network environment by integrating fuzzy logic and Q-learning. The experimental results validate the theoretical feasibility of the proposed approach. Further empirical data can be derived from training experiments on public datasets, enhancing the practical applicability of the proposed method.
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