What are the challenges in federated learning?

What are the main challenges in federated learning?

The main challenges in federated learning include data privacy, communication efficiency, and model convergence.

Can you explain data privacy issues in more detail?

Sure! Data privacy in federated learning is crucial because sensitive data remains on local devices, but ensuring that no private information is leaked during model updates is challenging.

What about communication efficiency?

Communication efficiency is a challenge because federated learning requires frequent updates between devices and the central server, which can be slow and costly, especially with large models.

How does model convergence work in this context?

Model convergence in federated learning can be difficult due to the non-iid nature of data across devices, leading to inconsistent updates and slower convergence rates.