Personalization Layer

Personalization layers in federated learning aim to improve the accuracy and efficiency of distributed machine learning models by adapting to the heterogeneity of data across different clients. Current research focuses on developing algorithms and model architectures that effectively incorporate these layers, often employing Bayesian neural networks or visual prompts to guide parameter selection and address data imbalances. This approach enhances the performance of federated learning, particularly in scenarios with non-independent and identically distributed data, leading to more robust and accurate models for applications like image classification and load forecasting.