The article "Federated Learning: Challenges, Methods, and Future Directions" by Tian Li provides an in-depth overview of federated learning, a paradigm where statistical models are trained at the edge of distributed networks, such as mobile phones or hospitals, while keeping data localized. The authors discuss the unique characteristics and challenges of federated learning, which differ significantly from traditional distributed environments due to issues like expensive communication, systems heterogeneity, statistical heterogeneity, and privacy concerns. They survey existing methods and approaches to address these challenges, including local updating methods, compression schemes, decentralized training, asynchronous communication, active device sampling, fault tolerance, and privacy-preserving techniques. The article also outlines several promising future research directions, emphasizing the need for interdisciplinary efforts to advance the field.The article "Federated Learning: Challenges, Methods, and Future Directions" by Tian Li provides an in-depth overview of federated learning, a paradigm where statistical models are trained at the edge of distributed networks, such as mobile phones or hospitals, while keeping data localized. The authors discuss the unique characteristics and challenges of federated learning, which differ significantly from traditional distributed environments due to issues like expensive communication, systems heterogeneity, statistical heterogeneity, and privacy concerns. They survey existing methods and approaches to address these challenges, including local updating methods, compression schemes, decentralized training, asynchronous communication, active device sampling, fault tolerance, and privacy-preserving techniques. The article also outlines several promising future research directions, emphasizing the need for interdisciplinary efforts to advance the field.