Breaking Privacy in Federated Learning

Breaking Privacy in Federated Learning

Despite the benefits of federated learning, there are still ways of breaching a user’s privacy, even without sharing private data. In this article, we’ll review some research papers that discuss how federated learning includes this vulnerability.

Despite the benefits of federated learning, there are still ways of breaching a user’s privacy, even without sharing private data. In this article, we’ll review some research papers that discuss how federated learning includes this vulnerability.

Federated learning is a new way of training a machine learning using distributed data that is not centralized in a server. It works by training a generic (shared) model with a given user’s private data, without having direct access to such data.

For a deeper dive into how this works, I’d encourage you to check out my previous blog post, which provides a high-level overview, as well as an in depth look at Google’s research.

Introduction to Federated Learning

Enabling on-device training, model personalization, and more

Federated learning has the major benefit of building models that are customized based on a user’s private data, which allows for better customization that can enhances the UX. This, as compared to models trained by the data aggregated at a data center that are more generic and may not fit the user quite as well. Federated learning also help save a user’s bandwidth, since they aren’t sending private data to a server.

Despite the benefits of federated learning, there are still ways of breaching a user’s privacy, even without sharing private data. In this article, we’ll review some research papers that discuss how federated learning includes this vulnerability.

The outline of the article is as follows:

  • Introduction
  • Federated Learning Doesn’t Guarantee Privacy
  • Privacy and Security Issues of Federated Learning
  • Reconstructing Private Data by Inverting Gradients

Let’s get started.

Introduction

Federated learning was introduced by Google in 2016 in a paper titled Communication-Efficient Learning of Deep Networks from Decentralized Data. It’s a new machine learning paradigm that allows us to build machine learning models from private data, without sharing such data to a data center.

The summary of the steps we take to do this is as follows:

  • A generic model (i.e. neural network) is created at a server. The model will not be trained on the server but on the users’ devices (the majority are mobile devices).
  • The model is sent to the users’ devices where the training occurs. So the same model (i.e. neural network) is trained parallelly on different devices, according to their private data.
  • Just the trained model (i.e. parameters or gradients) is shared back to the server.
  • The server averages the trained parameters from all devices to update the generic model based on the federated averaging algorithm.

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