Client Model

Client model research in federated learning focuses on collaboratively training machine learning models across decentralized clients without directly sharing data, aiming to improve model performance and efficiency while preserving privacy. Current research emphasizes addressing data heterogeneity across clients through techniques like cohorting similar clients, adaptive aggregation algorithms, and methods to incorporate information from unlabeled or partially labeled data. This work is significant because it enables the development of robust and accurate models in privacy-sensitive applications, such as healthcare and finance, where data is distributed across multiple institutions or devices.