1. Abid, A., Yuksekgonul, M., Zou, J.: Meaningfully debugging model mistakes using conceptual counterfactual explanations. In: International Conference on Machine Learning, pp. 66–88. PMLR (2022)
2. Bau, D., Zhou, B., Khosla, A., Oliva, A., Torralba, A.: Network dissection: quantifying interpretability of deep visual representations. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 6541–6549 (2017)
3. Cassara, P., Gotta, A., Valerio, L.: Federated feature selection for cyber-physical systems of systems. IEEE Trans. Veh. Technol. 71(9), 9937–9950 (2022)
4. Chen, C., Li, O., Tao, D., Barnett, A., Rudin, C., Su, J. K.: This looks like that: deep learning for interpretable image recognition. In: Advances in Neural Information Processing Systems, vol. 32 (2019)
5. Chen, F., Long, G., Wu, Z., Zhou, T., Jiang, J.: Personalized federated learning with graph. arXiv preprint arXiv:2203.00829 (2022)