Federated learning is a distributed machine learning technique where devices collaboratively train AI models without sharing raw data. Instead of sending sensitive information to central servers, devices only share model updates, ensuring privacy and security while advancing machine learning capabilities.

This approach emerged as a response to growing privacy concerns, with Google introducing the term in 2017. Companies like Apple have since adopted the technology, using it to train services like Siri while protecting user data.

The process works in five key steps:

1. A central server initializes a global model
2. Client devices receive copies and train on local data
3. Devices share only model updates
4. The server aggregates these updates
5. An improved global model is distributed back to devices

Common frameworks supporting federated learning include:

• TensorFlow Federated (Google)
• PySyft (PyTorch-based)
• Flower (open-source)

Types of Federated Learning:

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

Key Applications:

• Smartphones: Keyboard predictions, voice assistants
• Healthcare: Disease detection, treatment planning
• Financial Services: Fraud detection
• Autonomous Vehicles: Object detection, route planning
• Manufacturing: Predictive maintenance

Benefits:

• Enhanced data privacy and security
• Reduced central storage risks
• Lower communication costs
• Better model adaptation to local contexts
• Potential environmental benefits

Challenges:

• Communication efficiency
• Data protection from attacks
• Device and data heterogeneity
• Privacy guarantees
• User participation incentives

Federated learning differs from traditional machine learning by distributing the training process across devices, handling non-identical data distribution, reducing latency through edge computing, and eliminating the need for centralized data storage.

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