Introduction
In 2022, federated learning gained momentum as a privacy-focused approach to AI training. This decentralized method enables AI models to learn from data across multiple devices without sharing raw information, improving data security.
🔑 Benefits of Federated Learning
- Enhanced Privacy: Sensitive user data remains on personal devices.
- Reduced Bandwidth Usage: No need to transfer massive datasets to central servers.
- Improved AI Personalization: AI learns from user behavior without compromising privacy.
🚀 Industries Adopting Federated Learning
- Healthcare: AI diagnoses diseases without sharing patient data.
- Smartphones: AI assistants personalize recommendations privately.
- Finance: Banks use ML for fraud detection without centralizing customer data.
