Describe the concept of federated learning and its importance in privacy-preserving machine learning. What are the main challenges associated with implementing federated learning systems?

Federated learning allows models to be trained across multiple decentralized devices or servers without sharing raw data, thus preserving user privacy. The importance lies in its ability to utilize distributed data while complying with privacy regulations. Main challenges include ensuring communication efficiency, handling heterogeneous data across clients, addressing security vulnerabilities (such as model poisoning attacks), and managing the trade-off between local computation and global model performance.