🎯 Federated learning for agents: Protecting user privacy
Training AI models on decentralized data | Get started with AI | How-to guides and features
Federated learning is a groundbreaking approach to training AI models on decentralized data, such as data residing on users' mobile devices or within different organizations.
This technique enables virtual agents to learn from diverse datasets while preserving the privacy of individual data points.
This post explores the principles, techniques, and challenges of federated learning in virtual agent development.
This is a practical guide to help decision-makers and board members navigate this evolving landscape.
We must grapple with fundamental questions about the nature of identity, consciousness, and the very essence of human existence.
This is a new sub-series of the Deep Dive series “How to build with AI agents.” which aims to help you proactively address potential issues and empower your IT and support agents with automation tools and AI for faster case resolution and insights.
It follows the series “How to build with AI agents. "This time, we focus on building on your existing foundation and the unique aspects of AI agents.