Lately, the topic of security on machine learning is enjoying increased interest. This can be largely attributed to the success of big data in conjunction with deep learning and the urge for creating and processing over larger data sets for data mining. Since machine learning is becoming a part of day-to-day life, making use of our data, special measures must be taken to protect privacy.
In federated learning, the model is learned by multiple clients in a decentralized fashion. Here learning is shifted to the clients and only the learning parameters are centralized by the trusted curator. This curator the distribute aggregate model back to the client. The approach of federated learning can be widely used in mobile applications by considering the computational power and privacy aspects.
sharing model within certain users
When a model is learned in a conventional way, its parameters reveal information about the data that was used during training. In order to solve this problem discussion of differential privacy to learning algorithms has been developed. It is to ensure that the learned model does not know a client participate during decentralized training and the client’s data set will be protected from other client attacks.
1. Introduction
Basically, federated learning is the problem of training a shared global model under the coordination of a central server, from a federation of participating devices that maintain control of their own data. In standard machine learning approaches, it requires centralizing the training data on one machine or in a data center. But in federated learning, it enables mobile phones to collaboratively learn a shared prediction model while keeping all the training data on the device.
Data is often created on edge devices such as smartphones or IoT sensors attached to industrial equipment or is controlled by entities such as hospitals. Now, normally in machine learning when we train models, we move this data to the servers in our data center. But often the owners of these smartphones or sensors or these hospitals they can’t, or they won’t share the data with us because of privacy concerns or bandwidth challenges or both. Federated learning is an algorithmic solution to this problem it allows you to build a model while keeping the data at its source. When we do federated learning, each device or entity trains their own model locally and it’s that model that they share with the servers in the data center the server combines the model into a single federated model and it never has direct access to the training data in this way we help to preserve privacy and reduce communication costs in the cloud era. These topics will be discuses in the later sections of the review.
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