One of the most recent examples is the work by Tao et al. , who embarked on a cathode material sorting initiative for retired batteries. They leveraged existing battery data from various collaborators, including battery manufacturers, practical application operators, academic research institutions, and third-party platforms. Employing a collaborative and privacy-preserving ML approach, they developed a federated ML model. This model was trained using only one cycle of field-testing data from a unique dataset comprising 130 LIBs spanning 5 cathode materials and 7 manufacturers. This was accomplished through a standardized feature extraction process, without prior knowledge of historical operational conditions. The study compared the predictive power of their federated ML model with independently learned local models, under both homogeneous and heterogeneous battery recycling circumstances. To address the heterogeneity issue, the authors proposed a Wasserstein-distance voting strategy, which effectively mitigated cathode sorting errors to 1% and 3%. Additionally, an economic evaluation of retired battery recycling was conducted using the proposed federated ML framework, highlighting the importance of accurate sorting in the recycling process. Finally, the study delved into model interpretability, battery recycling implications, and the broader prospects of integrating federated ML into future battery recycling practices.