With increased demand on data privacy and security for AI models, Federated Learning technologies like Tensorflow Federated has become prominent in development and research. In this session, we will explore how federated learning using Tensorflow Federated enables collaboration across decentralized systems, allowing multiple organizations to build shared models without ever exposing their private data.

With the rise of privacy regulations like GDPR and CCPA, and increased scrutiny on data-sharing practices, federated learning offers a timely alternative that aligns with legal requirements and ethical data use. Additionally, recent advancements in edge computing and 5G have made it possible to deploy federated learning at scale, bringing AI capabilities directly to devices while maintaining user privacy.

We will also dive into the core principles of federated learning, discussing its architecture, algorithms, and real-world applications in industries like healthcare, finance, and IoT. Attendees will gain insights into the benefits and challenges of this innovative approach and its role in creating a more privacy-conscious future for artificial intelligence.