Consensus Learning

Consensus learning is a distributed machine learning paradigm aiming to achieve agreement among multiple models or agents, often to improve robustness, efficiency, or privacy. Current research focuses on applying consensus learning to diverse tasks, including node classification on graphs, essential matrix estimation, and federated learning across heterogeneous datasets, employing techniques like deep sets, graph neural networks, and vision transformers. This approach is significant for enabling collaborative learning in privacy-sensitive settings, scaling machine learning to large datasets, and improving the accuracy and robustness of models in various applications.