Apr 17 2025
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Post Detail
As data privacy becomes a global priority, Federated Learning (FL) is gaining attention as a method to train machine learning models across decentralized devices without transferring raw data.
How It Works
Federated Learning moves the model — not the data. It sends a global model to each device (e.g., smartphones, hospitals), where it is trained locally. The model updates are then sent back to a central server and aggregated to improve performance.
Advantages
- Privacy: Sensitive data never leaves its source.
- Security: Reduces the risk of centralized data breaches.
- Scalability: Utilizes edge devices and local resources for training.
Use Cases
- Healthcare: Hospitals train diagnostic models without sharing patient records.
- Finance: Collaborate across banks to improve fraud detection.
- Smartphones: Personalized AI on-device (e.g., keyboard suggestions).
- IoT and Smart Cities: Devices like sensors and traffic lights contribute without exposing personal data.
Enhanced Techniques
- Differential Privacy: Adds statistical noise to updates.
- Homomorphic Encryption: Enables computation on encrypted data.
- Secure Aggregation: Ensures updates are aggregated without exposure.
Challenges
- Non-IID Data: Devices may have very different types of data.
- Hardware Variability: Inconsistent processing power across devices.
- Latency and Communication Costs: Frequent syncing can be slow or expensive.
Federated Learning represents a pivotal shift toward decentralized, user-centric AI — especially important in sectors where trust and data ownership are non-negotiable.
