New Survey Maps Interpretable Federated Learning Landscape

This paper provides the first comprehensive survey of interpretable federated learning (IFL), a research area focused on making federated learning models understandable. The authors introduce a new taxonomy for IFL that categorizes methods for explaining predictions, aiding model debugging, and revealing data owner contributions. The survey analyzes existing IFL approaches, evaluation metrics, and suggests future research directions. AI

RANK_REASON The cluster contains an academic paper that surveys a subfield of machine learning. [lever_c_demoted from research: ic=1 ai=1.0]

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• Anran Li
• arXiv
• Federated learning
• Interpretable federated learning

• paper
• other

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