Client Collaboration

Client collaboration in machine learning focuses on training shared models across multiple clients while preserving data privacy and addressing data heterogeneity. Current research emphasizes developing algorithms and model architectures (like personalized federated learning and various adaptations of large language models) that efficiently handle non-independent and identically distributed (non-IID) data, optimize communication costs, and account for varying client resources and capabilities. This field is significant for enabling large-scale machine learning applications while respecting data privacy regulations and improving model performance in diverse settings, with applications ranging from finance to healthcare.