Federated Setting

Federated learning (FL) addresses the challenge of collaboratively training machine learning models on decentralized data without compromising individual data privacy. Current research focuses on improving FL's efficiency and robustness in handling heterogeneous data, exploring techniques like model personalization (e.g., using adaptors or knowledge distillation), addressing label imbalance, and developing privacy-preserving aggregation methods. This field is significant because it enables large-scale machine learning on sensitive data across diverse sources, with applications ranging from healthcare and IoT to industrial process optimization and drug discovery.