Federated learning is a distributed machine learning technique where devices train AI models collaboratively without sharing raw data. Instead of centralizing data collection, each device trains a local model and shares only model updates, ensuring privacy and security.

This approach emerged as a response to growing privacy concerns, with Google introducing the term in 2017. Companies like Apple have since adopted it for applications like Siri training and speech recognition.

How Federated Learning Works:

1. A central server initializes a global model
2. Client devices receive copies of the model
3. Devices train the model using local data
4. Devices share only model updates back to the server
5. Server aggregates updates to improve the global model

Popular Frameworks:

• TensorFlow Federated
• PySyft
• Flower

Types of Federated Learning:

• Centralized: Uses a central server for coordination
• Decentralized: Devices communicate peer-to-peer
• Heterogeneous: Involves various device types
• Cross-Silo: Organizations collaborate while keeping data private

Key Applications:

• Smartphones: Keyboard predictions, voice assistants
• Healthcare: Disease detection, treatment planning
• Finance: Fraud detection
• Transportation: Autonomous driving systems
• Manufacturing: Predictive maintenance

Benefits:

• Enhanced data privacy and security
• Reduced communication costs
• Improved model adaptation to local contexts
• Compliance with data protection regulations

Challenges:

• Communication efficiency
• Data protection from attacks
• Managing device heterogeneity
• Ensuring user participation
• Maintaining model accuracy

Federated learning differs from traditional machine learning by distributing the training process across devices, improving privacy, and reducing latency through edge computing. This approach is particularly valuable in sectors where data confidentiality is crucial, such as healthcare and finance.

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