Federated Learning
What is Federated Learning?
Federated Learning is a decentralized machine learning approach that trains AI models across multiple edge devices without sharing raw data. Instead of centralizing data, it sends model updates to a central server for aggregation—enhancing privacy, speed, and compliance in Edge AI environments.
Why Is It Used?
Federated Learning is used to enable privacy-preserving AI at the edge. It eliminates the need to move sensitive data to the cloud, reducing latency and risk while complying with data protection regulations like GDPR. This makes it ideal for distributed IoT, healthcare, finance, and smart city ecosystems.
How Is It Used?
In an Edge AI setup, local devices (e.g., sensors, gateways, smartphones) train small models on-device using their own data. These models then send encrypted weight updates—not the actual data—to a central aggregator. The global model is updated iteratively, improving with each round of distributed learning.
Types of Federated Learning
Horizontal Federated Learning: Devices share similar features but have different data samples (common in healthcare or finance).
Vertical Federated Learning: Devices share data with different features but on the same entities.
Federated Transfer Learning: Combines the benefits of both, optimizing learning across limited or diverse datasets.
Benefits of Federated Learning
Enhanced Data Privacy – Keeps sensitive information local.
Low Latency – Reduces reliance on cloud computing.
Regulatory Compliance – Supports GDPR and regional privacy laws.
Scalable Collaboration – Enables learning from diverse edge environments.
Improved AI Accuracy – Aggregates diverse model insights for stronger global performance.