Client Heterogeneity

Client heterogeneity in federated learning (FL) refers to the variations in data distributions, computational resources, and communication capabilities across participating clients. Current research focuses on developing algorithms that address this heterogeneity, often employing techniques like submodular maximization for equitable client selection, personalized model aggregation based on client similarity, and efficient data distribution estimation to improve model accuracy and fairness. Overcoming client heterogeneity is crucial for the successful deployment of FL in real-world applications, enabling privacy-preserving collaborative training across diverse and distributed datasets while ensuring model performance and fairness.