Cross Silo Federated Learning

Cross-silo federated learning (FL) focuses on collaboratively training machine learning models across multiple organizations (silos) without directly sharing sensitive data. Current research emphasizes developing privacy-preserving mechanisms, such as differential privacy and secure multi-party computation, to mitigate data leakage risks, particularly concerning individual subjects within silos, and improving communication efficiency through techniques like RDMA and optimized aggregation strategies. This field is crucial for enabling large-scale collaborative AI development in sensitive domains like healthcare and finance while adhering to stringent privacy regulations, driving advancements in both theoretical understanding and practical applications of distributed machine learning.