What exactly is federated learning?
Federated learning is a way to train AI models without anyone seeing or touching your data, offering a way to unlock information to feed new AI applications. It's like giving your AI a private tutor without revealing all your data secrets. Let's break it down.
First, we need to get the lowdown on traditional machine learning. It usually involves your data hanging out on one big server, but with billions of super-smart mobile devices in the world, we're talking a huge leap in computing power. Your phone might soon match laptops in capacity, and the data it generates is off the charts. This opens up possibilities for super-accurate and personalized AI models.
We need to understand what traditional or centralized machine learning is before delving into federated machine learning.
Almost everyone in the world has personal devices due to which we are witnessing a new surge in the volumes of data generated — something never observed in the past, and this is increasing at an exponential rate. With data getting generated at an ever-increasing pace, it has opened up new possibilities for providing more accurate and personalized ML models that can enhance customer experience and help them make decisions.
Centralized machine learning - Now, centralized machine learning is the usual deal. You create an algorithm using sample data, and a machine learns patterns from it. Five steps get it done: identify the problem, prep data, train on a central server, send the model to client systems, and start predicting results on new data. But, there's a catch—it raises security and privacy concerns. Ever wondered how Google Maps suggests routes in real-time? It's all about Google collecting data from vehicles, computing the best route, and sending it back to you. But, centralized learning has its drawbacks—it risks privacy violations and data breaches, and governments are tightening rules to protect user data.
What is the Solution?
Federated Learning steps in - It's a new branch in AI that taps into scattered data and local device computing power, offering a personalized experience without compromising user privacy. How? With homomorphic encryption, information can be shared between a client and server without exposing user privacy. The server aggregates learnings from clients, improving the shared model. How does it work? Start with training a generic model on a central server, send it to user devices, and let local models on those devices learn and improve with time. Clients periodically send learnings to the central server, using homomorphic encryption to protect user data. The server aggregates and improves the shared model, and the cycle repeats. The potential? Huge! Imagine self-driving cars using federated learning to share info about obstacles and enhance safety. The future looks promising—new applications, personalized experiences, and even rewards for users who contribute to the learning process. Companies like Google (Tensorflow Federated) and OpenMined are already in the game, ensuring data protection and paving the way for an exciting future in federated learning.
How does federated learning work?
Federated learning works by training machine learning models across decentralized devices instead of relying on a central server. Here's a step-by-step breakdown:
1. Initialization:
A generic machine learning model is created on a centralized server. This model serves as a starting point and is not personalized.
2. Model Distribution:
The initial model is sent to individual user devices or clients. These clients can range from hundreds to millions, depending on the application's user base.
3. Local Training:
Clients use their local data to train the model. This training process occurs on the user's device without sharing the raw data with the central server.
4. Update Transmission:
After local training, the client sends only the model updates or changes back to the central server. Importantly, the actual user data stays on the client and is not transmitted.
5. Aggregation:
The central server collects and aggregates these model updates from all clients. It uses this collective information to improve the global model.
6. Model Update:
The updated global model is then sent back to the clients. This updated model now reflects the learning from all individual devices.
7. Iterative Process:
Steps 3 to 6 are repeated iteratively. With each round, the global model becomes more accurate and personalized without the need to access individual user data directly.
Key Characteristics:
Privacy-Preserving: Federated learning is designed to keep user data decentralized and private. Only model updates, not raw data, are transmitted.
Decentralization: Computation occurs on local devices, utilizing the computing power of each device and avoiding the need for a centralized processing unit.
Continuous Learning: The process is iterative, allowing the model to continuously learn and improve based on the collective knowledge from all participating devices.
Future of Federated Learning
Self-driving connected cars can leverage federated learning to drive safely. Instead of avoiding a pothole just based on a predetermined set of algorithms and rules, if a self-driving car utilizes the information from all cars that crossed the same pothole in last 1 hour, it will definitely be able to make a better decision in terms of safety and comfort of the passenger.
The next 5 years are going to be very interesting for federated learning. We will see a plethora of new applications taking advantage of Federated learning, enhancing user experience in a way that was not possible before. A lot of companies will come forward and provide a platform for developing federated learning applications quickly. We will see an era where the user will be rewarded for sharing their local learnings with big companies.
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