mixedfed
Mixedfed is a term used in the field of federated learning to describe approaches that blend information from multiple heterogeneous data sources within a cooperative training framework. The aim is to improve model generalization and robustness when client data are non-identically distributed and originate from different domains or tasks. Mixedfed methods seek to leverage diverse data without centralizing raw data, preserving privacy while enhancing performance on unseen or cross-domain data.
Conceptually, mixedfed encompasses strategies that mix data or model information across clients. This can include data-level
Key challenges include managing data heterogeneity, ensuring privacy and security, handling communication efficiency, and addressing fairness
Related concepts include traditional federated learning, horizontal and vertical federated learning, and federated transfer learning. See