How Federated Learning Works: Key Benefits and Practical Examples

What is federated learning, and how does it work?

Federated learning means "a decentralized machine learning approach where multiple devices or servers work together to train an AI model without exchanging raw data. Each device trains the model using its own information. Then, it sends only the updates to a central server, which combines these updates to improve the main model."

The process has four main steps:

• Model initialization: In this phase, a central server creates a starting model and sends it to several devices, such as phones, sensors, or small servers. The server provides instructions about training, which includes the total number of rounds and other settings.
• Local training: In the local training step, each device uses only its own data to train the model. After comparing the model's predictions with the right answers, the device updates the model to increase accuracy. It repeats this process several times according to the instructions. Once training is complete, each device calculates how the model changed, which are called local updates.
• Sharing and aggregation of updates: After training, devices share their updates with the server rather than sending the original data. The server then mixes all these updates together, usually by averaging them, to create a new global model. It may apply extra security methods to ensure no one can tell which device contributed which update.
• Model distribution: Finally, all devices receive the updated global model from the server at the end, which then starts the next round of training to gain more knowledge and accuracy.

What are the three types of federated learning in AI?

Federated learning can work in different ways depending on how the data is shared. The three main types are:

• Horizontal Federated Learning: This happens when different groups have the same kind of data but for different people. Consider, for instance, a number of hospitals in different cities that gather patient data (which includes vital signs, diagnoses, and blood test results), and only send updates to a central server. The server then combines these updates to train a model that learns from all hospitals together, without ever seeing personal patient records.
• Vertical Federated Learning: This is used when groups have data about the same people, but each holds different kinds of information. For example, an online retailer is aware of a customer's past purchases, and a bank is aware of the customer's credit score. Together, the bank and the store can train a model to identify fraud or make product recommendations, but each conceals its shortcomings. VFL works well when data features are different, but the users or sample IDs are the same.
• Federated Transfer Learning: FTL applies when participants have completely different people and different types of data. Let's say a small retailer in one city and a larger retailer in another city. The small retailer doesn't have much data to train a recommendation model. However, he can take advantage of the larger retailer's model by using FTL. Even if the customers and data features are different, transfer learning techniques use patterns from one dataset to another.

Pippit AI: Empowering users in decentralized content creation

Pippit is an all-in-one toolkit for businesses to create high-quality marketing materials for personal branding, social media updates, or ads. It lets you instantly convert your text input to engaging videos or images in minutes. Not only that, but it supports more than 28 languages and allows you to import your products, customize visuals, and edit the content to perfection before sharing them on social or professional platforms.

Key features of Pippit for decentralized content creation

Pippit AI offers features that support decentralized content creation to give the option to produce professional content from your devices while ensuring privacy.

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1. AI-powered video agent mode for smart content creation

Pippit's Agent mode can turn a text prompt into a full video. Simply, enter a prompt, paste your link, upload media files, or bring in a document, and let AI generate videos for you in minutes. It writes the script in different languages and adds captions, voice, and avatars automatically. This means you can create videos locally without sending your raw data anywhere.

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1. Customizable digital avatars

With Pippit, you can choose from an avatar library or create one from your own image to add a voice and use it in your videos. This lets you control your digital identity while producing content for social media, marketing, presentations, and more.

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1. Quick image generation with an AI design tool

The AI design tool in Pippit uses the Nano Banana text-to-image model by Google DeepMind to create images from your simple text description. Not only that, but you can use its AI inpaint and outpaint options to edit your photos and add or restore elements. It even lets you enhance your photo quality or use the eraser to remove unwanted objects in the background.

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1. Seamlessly integrate with e-commerce platforms

You can easily integrate and import your products from your Shopify or TikTok store to your Pippit account. You can then use the images or clips to create engaging Shopify product videos or promo posters using AI. It also lets you bring in the product details in CSV format and add a shoppable link to your videos while sharing them to your TikTok account.

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1. Bring static photos to life

The "AI talking photo" tool in Pippit takes your portrait picture and converts it into a talking avatar video. It lets you add a script, choose a voice, and overlay captions or upload your audio recording for the avatar to speak. It also has preset talking photo templates and a library of trending audios to choose from.

What are the main advantages of federated learning models?

Federated learning frameworks bring several benefits to the table that improve how AI systems learn and become safer and more practical to use in real-world settings:

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1. Improved data privacy: As federated learning trains models right on your device, your personal details never leave it. This protects your sensitive data and lowers the risk of leaks, hacking, or misuse.
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2. Reduced data transfer: Rather than sending entire datasets to a central server, your devices only send updates or changes to the model. This cuts down the amount of data traveling over the network and the demand for bandwidth.
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3. Enhanced security and compliance: Since raw data stays local, decentralized federated learning supports strong security measures. Organizations can follow privacy rules and legal requirements more easily and reduce the risk of data breaches.
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4. Scalability on different devices: FL is compatible with a variety of devices, ranging from large servers to smartphones. It lets many devices work together to train a model, using their own data to slowly make the system smarter over time.

What are examples of federated learning models?

• Google Assistant: To enhance voice recognition, Google uses federated learning in its Assistant. This means your personal audio never leaves the phone because the AI is trained directly on your device.
• Autonomous vehicles: With NVIDIA's FLARE platform, self-driving cars in different countries can train models together. Each vehicle shares local insights while still following privacy rules, which can improve the global system..
• Robotics: Robots use federated learning to improve how they move, make decisions, and complete tasks. The FLDDPG system, for instance, uses FL in swarm robotics. Even in locations with poor or limited communication, the group can improve navigation and decision-making because each robot trains locally and shares model updates.
• Healthcare: The MedPerf platform uses federated learning to test and improve medical AI models in multiple hospitals. Local updates are combined through model updates, which lets the AI perform well on real-world data while protecting patient information and ensuring privacy.

Conclusion

In this article, we've explored what federated learning is, how it works, and its three main types. We've also shared its advantages and real-life examples that showed how this technology works in practice. Pippit AI uses similar principles in content creation and lets you generate videos, images, and avatars while maintaining control of your data. Start using Pippit today and create content that respects privacy.

FAQs

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1. What is decentralized federated learning?

Decentralized federated learning trains AI models on multiple devices or organizations using their own data and shares only the updates. This protects privacy, reduces data transfer, and lets the model learn from different sources. With Pippit, you can create videos, images, and avatars on your device. You can generate scripts in multiple languages, edit images, and customize avatars while your original files stay on your device.

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1. Is there any free tutorial on federated learning?

Yes, several free federated learning courses, step-by-step guides, and demos are available online that show how models are trained locally on devices and how updates are shared to improve a global model. With Pippit, you can apply a similar approach to content creation. You can generate videos with automated captions and voices, design images or edit them with AI upscale, inpainting, or outpainting, and create AI avatars using your photos. Pippit lets you experiment with these features directly on your device, so you can explore and practice content creation while your files stay private.

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1. Is Google using federated learning?

Yes, Google uses federated learning in several of its products, such as Google Assistant and smartphone keyboards. With Pippit, you can take a similar hands-on approach to creating content for digital marketing, product promotion, and more. All of this happens on your device, so your original media stays private while you experiment with creative features.