What is data heterogeneity in federated learning?

What is data heterogeneity in federated learning?

Data heterogeneity in federated learning refers to the differences in data distributions across various devices or clients participating in the training process. This can lead to challenges in model convergence and performance.

Why is it a problem for federated learning?

It's a problem because varying data distributions can cause the global model to perform poorly on some clients, leading to biased or inaccurate predictions.

How can we address data heterogeneity?

We can address it by using techniques like personalized federated learning, data augmentation, or adaptive optimization methods to better accommodate the diverse data.

Can you give an example of personalized federated learning?

Sure! In personalized federated learning, each client can train a local model tailored to its specific data while still benefiting from the global model updates, allowing for better performance on individual tasks.

What are the implications of data heterogeneity for privacy?

Data heterogeneity can enhance privacy since clients do not need to share their raw data. However, it also raises concerns about the representativeness of the global model.