What is Federated Learning

With the concerns about data privacy rising and with the rise of AI models, people are increasingly concerned about their data being misused for purposes beyond their consent. As we know that data is the new oil of this century, it is a very sought after commodity. Seeing these concerns, Google introduced the concept of federated learning back in 2016. Let’s delve into this topic a bit and understand what it is.

Understanding Federated Learning

Federated Learning is like a team of learners working together without revealing their individual knowledge. I am not sure what analogies you can put forward for this, but the one that I can think of is Shamir’s Secret Sharing. In traditional learning, all data needs to be sent to a central server where the model is trained, but not with federated learning.

How Federated Learning Works

  1. Decentralized Learning: federated learning is a decentralized model just like blockchain, where each device(PC or mobile) collects and trains the model on the device itself. No data is transmitted to the central servers. These local models are a summary of the data that your device has discovered.
  2. Collaboration: The data that is shared with the central server is the model itself and not your original data, this ensures that your data is not directly accessible to any intruder, unless and until they hack your device itself.
  3. Combining Knowledge: Once all the model’s data is received, some aggregation or perhaps other kinds of model combination algorithm is used to get a combined model which then serves the purpose intended.
  4. Iterative Process: This process continues over time as the device use continues and the models improve over time.

Benefits of Federated Learning

  1. Privacy Protection: Needless to say, the process is obviously more secure as your data is never transmitted to remote servers.
  2. Efficiency: Rather than training a single model with all the data collected, federated learning turns out to be super effective in training all the models locally, leaving the servers with only the load to aggregate.
  3. Personalization: Since the models are trained locally on your device, they become more personalized over time, performing better on an individual basis.

Applications of Federated Learning

  1. Smartphones: It’s been used for a long time in your keyboards when the keyboard automatically suggests what you are going to type (scary).
  2. Healthcare: It has some applications in the medical sector where data from the patient can be used without compromising their identity.
  3. Recommendation Systems: Streaming services like Netflix and Prime already use this to cater the best content to you.

Overall, federated learning is a very promising technology which solves a very crucial problem. Solving privacy whilst also making the whole process more efficient is like hitting two birds with a stone. With the growth in the use of personal computing devices, from watch to televisions more advacnes are being made in this area to make it mroe secure and reliable.

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