Cafe: Improved Federated Data Imputation by Leveraging Missing Data Heterogeneity.

Area of Science:

• Machine Learning
• Data Science
• Decentralized Systems

Background:

• Federated learning (FL) is a decentralized approach that enhances performance while preserving data autonomy and confidentiality.
• Handling missing values in FL is an underexplored area, particularly with heterogeneous data distributions across clients.
• Current state-of-the-art (SOTA) federated imputation methods exhibit significant performance degradation in heterogeneous settings.

Purpose of the Study:

• To investigate federated imputation methods for missing data, focusing on complex scenarios with data heterogeneity.
• To address the limitations of existing SOTA approaches in maintaining imputation quality under data heterogeneity.
• To propose a novel personalized federated learning approach for improved missing data imputation.

Main Methods:

• Introducing Cafe, a personalized federated learning approach for missing data imputation.
• Leveraging observed and missing data distribution differences across clients to enhance imputation quality.
• Developing personalized imputation models by computing automatically calibrated weights for varying levels of heterogeneity.

Main Results:

• Cafe matches SOTA baseline performance in homogeneous data settings.
• Cafe significantly outperforms SOTA baselines in heterogeneous data settings.
• Empirical evaluations confirm Cafe's effectiveness across diverse settings.

Conclusions:

• Cafe offers an effective solution for federated missing data imputation, particularly in challenging heterogeneous environments.
• Personalized weighting strategies in Cafe adapt to data heterogeneity, improving imputation accuracy.
• The proposed approach advances the field of federated learning by addressing critical data quality issues.