Federated Learning: Privacy-Preserving AI
Federated Learning: Privacy-Preserving AI Artificial intelligence (AI) has become ubiquitous in our daily lives, powering applications such as spam filters, chatbots, and recommendation systems. However, these applications rely on massive amounts of data, often collected from users' personal devices or online activities, to train and improve their AI models. This raises serious concerns about data privacy, security, and ownership, as users may not want to share their sensitive information with third parties, or may face legal or ethical restrictions on doing so. Moreover, centralizing data in one place also poses challenges for scalability, efficiency, and robustness, as data may be too large, too diverse, or too dynamic to be transferred and processed by a single server. Federated learning is a novel machine learning technique that aims to address these challenges by enabling multiple parties to collaboratively train a common AI model without exchanging their data . Instead of s...