Artificial Intelligence

   

The Principle, Communication Efficiency and Privacy Issues of Federated Learning

Authors: Hakan Uzuner

Standard machine learning approaches require a huge amount of training data to be stored centralized in order to feed the learning algorithms. Keeping and using data centralized brings many negative aspects with it. Those aspects can be inefficient communication between the centralized data center and the clients producing the data, privacy issues and quick usability of the profits and results of the training. Google’s new approach, federated learning, on the other hand tackles all these problems. The training data is kept decentralized at the client’s devices while communicating only with small updates of the common model. This method allows for optimizations of communication, keeping the privacy of users involved in the process and providing quick usability of the model’s process. In this paper I will explain how the federated learning principle works. Further on, I will give a small insight on optimization possibilities of communication efficiency as well as on privacy issues involved in machine learning processes and how those can be solved using federated learning principles. Additionally, I will show the connection between the federated learning concept and organic computing.

Comments: 5 Pages.

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Submission history

[v1] 2019-03-10 05:27:02

Unique-IP document downloads: 13 times

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