Cross-silo federated learning (CS-FL) is a distributed learning setting which allows an identified set of organizations to collaboratively train a single global model. Since CS-FL use cases are often heterogeneous, it may be more appropriate to dynamically provide different models to more homogeneous subfederations. In addition, such systems can be undermined by contributions of poor quality, making negligent or even malicious participants critical to consider. However, distinguishing such participants in a heterogeneous context is especially difficult. We present RADAR, a novel architecture for CS-FL able to assess the quality of the participants' contributions, regardless of data similarity. RADAR leverages client-side evaluation to directly collect feedbacks from the participants. The same evaluations allow grouping participants according to their perceived similarity and weighting the model aggregation based on their reputation. To evaluate our approach on concrete experiments, we implement a collaborative intrusion detection system (CIDS) scenario and test our architecture in various data-quality settings using labelflipping. Our results confirm that combining clustering and a reputation system succeeds in detecting a wide range of Byzantine behaviors, including colluding attackers, which highlights RADAR's versatility.