Collaborative Machine

Collaborative machine learning focuses on training machine learning models across distributed devices or systems, aiming to improve efficiency, privacy, and performance compared to centralized approaches. Current research emphasizes efficient communication architectures, particularly in federated learning, often employing model compression and algorithms like federated averaging, and exploring the use of large language models for automation and optimization. This field is significant for its applications in diverse areas like anomaly detection in online services, augmented reality, and healthcare, offering improved scalability and data privacy while addressing challenges like fairness and efficient resource utilization.