Federated learning
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Federated Learning is a decentralized machine learning approach that enables model training across multiple devices or servers while keeping the data localized. In traditional machine learning, data is typically collected from various sources and centralized for model training. Federated Learning, however, allows training directly on the devices or servers where the data resides, without the need to send the raw data to a central server.
Here’s how Federated Learning works:
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Setup: A central server (often referred to as the “coordinator”) initiates the training process by sending an initial model to participating devices or servers.
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Local Training: Each device or server performs model training using its local data. This is done iteratively, with each device updating the model using its data while not sharing the data itself.
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Model Aggregation: After local training iterations, the updated models from the devices are sent back to the central server. The central server aggregates these models to create a global model that benefits from the insights of all the devices without directly accessing their data.
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Model Update: The global model is then updated based on the aggregated models. This process may involve techniques such as averaging or weighted averaging.
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Iteration: Steps 2-4 are repeated iteratively. The central server sends the updated global model to the devices for further local training, and the process continues.
Federated Learning offers several advantages:
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Privacy: Data remains on the local devices or servers, ensuring that sensitive information is not exposed. Only model updates are shared, enhancing privacy and security.
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Efficiency: Since data doesn’t need to be sent to a central server, Federated Learning reduces the communication overhead and potential bandwidth constraints associated with sending large datasets.
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Decentralization: Federated Learning is well-suited for scenarios where data is distributed across multiple devices or locations, making it feasible to leverage insights from disparate sources.
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Real-time Learning: Devices can continuously train models using the most up-to-date data, leading to models that adapt to changing conditions in real-time.
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Reduced Data Transfer: By only transferring model updates, Federated Learning minimizes the amount of data that needs to be sent over the network.
Federated Learning is particularly relevant in situations where data privacy, security, or regulatory concerns prevent data from being centralized. It’s commonly used in scenarios such as mobile devices (smartphones), edge computing, Internet of Things (IoT) devices, and in organizations that want to collaborate on model training without sharing sensitive data.
However, Federated Learning also comes with challenges, such as handling communication issues, dealing with data heterogeneity across devices, and addressing potential biases introduced by unevenly distributed data. Researchers and practitioners are actively working on addressing these challenges to further advance the adoption of Federated Learning in various domains.
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