Non-IID federated learning with Mixed-Data Calibration

Abstract

Federated learning (FL) is a privacy-preserving and collaborative machine learning approach for decentralized data across multiple clients. However, the presence of non-independent and non-identically distributed (non-IID) data among clients poses challenges to the performance of the global model. To address this, we propose Mixed Data Calibration (MIDAC). MIDAC mixes M data points to neutralize sensitive information in each individual data point and uses the mixed data to calibrate the global model on the server in a privacy-preserving way. MIDAC improves global model accuracy with low computational overhead while preserving data privacy. Our experiments on CIFAR-10 and BloodMNIST datasets validate the effectiveness of MIDAC in improving the accuracy of federated learning models under non-IID data distributions.

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