Collaborative Machine Learning

Collaborative machine learning focuses on training machine learning models across multiple decentralized devices while preserving data privacy. Current research emphasizes addressing challenges like data heterogeneity through personalized models, robust aggregation techniques (e.g., using randomized aggregation or support vector machines), and secure communication protocols in decentralized or peer-to-peer settings. This field is significant for enabling large-scale model training with sensitive data, fostering trust and fairness among participants, and improving the efficiency and robustness of machine learning systems in various applications.