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New Survey Maps Interpretable Federated Learning Landscape

By PulseAugur Editorial · [1 sources] ·

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]

Read on arXiv cs.LG →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

New Survey Maps Interpretable Federated Learning Landscape

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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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COVERAGE [1]

  1. arXiv cs.LG TIER_1 English(EN) · Anran Li, Rui Liu, Ming Hu, Yuanyuan Chen, Shipeng Wang, Lizhen Cui, Han Yu ·

    Towards Interpretable Federated Learning

    arXiv:2302.13473v2 Announce Type: replace Abstract: Federated learning (FL) enables multiple data owners to build machine learning models collaboratively without exposing their private local data. In order for FL to achieve widespread adoption, it is important to balance the need…

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