Research on federated learning involves exploring the forefront of machine learning and decentralized computing. This innovative approach distributes model training across multiple devices while safeguarding individual data privacy. Researchers focus on developing algorithms, communication protocols, and security measures to enable collaborative learning without compromising privacy. The aim is to address challenges like model aggregation and communication overhead, paving the way for efficient, privacy-preserving federated learning systems that have the potential to reshape the landscape of AI ecosystems.
