Federated Learning: Training AI Models Without Centralized…

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Federated Learning: Training Models Without Centralized Data

Federated learning represents a paradigm shift in machine learning, enabling model training across distributed devices while keeping data local and private. This comprehensive guide explores the principles, algorithms, and applications of this revolutionary approach to collaborative AI.

Model Distribution: Send current global model to selected clients

Local Training: Each client trains a model on their own local data.

Model Aggregation: The server then averages the updates from all the clients to create an improved global model.

live demo · related simulation● LIVE

import torch.nn as nn

from syft.workers import VirtualWorker

# Create virtual workers (clients)

Frequently asked questions

How do I run the federated learning process?

Run federated learning

What does `state = federated_averaging_process.init` do?

state = federated_averaging_process.initialize()

What is the purpose of the `for round_num in range(10):` loop?

This loop iterates ten times, representing the training rounds within the federated learning process.

How do I retrieve the latest state and metrics from the federated averaging process?

The `federated_averaging_process.next(state, federated_train_data)` function updates the current state and collects relevant performance metrics after each round of training.

▶ Try it live

Everything above runs in your browser — open Hash Function Avalanche Visualizer and change the parameters while it is running. Nothing is installed, nothing is uploaded, the whole model lives in one tab.

▶ Open Hash Function Avalanche Visualizer simulation

Federated Learning: Training AI Models Without Centralized Data | AI with Skakun

Home > Categories > AI Research > Federated Learning

Model Distribution: Send current global model to selected clients

import torch.nn as nn

Frequently asked questions

▶ Try it live

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