Introduction

The next generation of artificial intelligence is built upon the core idea revolving around “data privacy”. When data privacy is a major concern and we don’t trust anyone withholding our data we can turn to federated learning for building privacy-preserving AI by building intelligent systems privately.

Federated learning is about moving computations to data. Where a globally shared model is bought to where the data is e.g. Smartphones. By moving the model to the device, we can collectively train a model as a whole.

With this concept in mind, anyone can take part in Federated learning on their devices either directly or indirectly, E.g. Edge devices such as smartphones and IoT devices can benefit from on-device data without the data ever leaving the device especially for computationally constrained devices where communication is a bottleneck with smaller devices.

The concept of moving computations to data is a powerful concept in terms of building any intelligent system while protecting the privacy of any individuals.

Federated learning also comes in three categories such as “Horizontal federated learning”, “Vertical federated learning”, and “Federated transfer learning”.

Horizontal federated learning uses datasets with the same feature space across all devices, this means that Client A and Client B has the same set of features as shown in a) below. Vertical federated learning uses different datasets of different feature space to jointly train a global model as shown in b) below. One example would be Client A (Amazon) has information about the customer’s movie purchases on Amazon, and Client B (IMDB) has information about the customer’s movie reviews, using these two sets of datasets from different domains allows one to serve the customers better using movie reviews information (IMDB) to provide better movie recommendation to the customers browsing movies in Amazon. Lastly, Federated transfer learning is vertical federated learning utilized with a pre-trained model that is trained on a similar dataset for solving a different problem. One such example of Federated transfer learning is to train a personalised model e.g. Fitness tracker to monitor the users exercising habits.

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