Contribution Evaluation

Contribution evaluation assesses the relative importance of individual participants' contributions in collaborative machine learning settings, particularly within federated learning (FL) and online deliberations. Current research focuses on developing accurate and efficient methods, often employing techniques like Shapley values from game theory, large language models for qualitative assessment, and novel metrics based on model parameters and gradients to avoid reliance on validation datasets. These advancements are crucial for ensuring fairness, incentivizing participation, and improving the robustness and efficiency of collaborative machine learning systems across diverse applications.