Client Data Heterogeneity

Client data heterogeneity in federated learning (FL) poses a significant challenge, hindering the effectiveness of collaboratively trained global models due to variations in data distributions across participating clients. Current research focuses on developing algorithms that address this heterogeneity, employing techniques like personalized federated learning (PFL), aggregation-free methods, and generative models (e.g., GANs) to improve model accuracy and convergence. These advancements are crucial for enabling the wider adoption of FL in real-world applications where data privacy and decentralized training are paramount, impacting fields ranging from medical imaging to satellite analysis.