Federated Learning for the Future

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.

#federated-learning #mls #programming #machine-learning #data-science #deep learning

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Federated Learning for the Future

Federated Learning for the Future

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.

#federated-learning #mls #programming #machine-learning #data-science #deep learning

Jerad  Bailey

Jerad Bailey

1598891580

Google Reveals "What is being Transferred” in Transfer Learning

Recently, researchers from Google proposed the solution of a very fundamental question in the machine learning community — What is being transferred in Transfer Learning? They explained various tools and analyses to address the fundamental question.

The ability to transfer the domain knowledge of one machine in which it is trained on to another where the data is usually scarce is one of the desired capabilities for machines. Researchers around the globe have been using transfer learning in various deep learning applications, including object detection, image classification, medical imaging tasks, among others.

#developers corner #learn transfer learning #machine learning #transfer learning #transfer learning methods #transfer learning resources

sophia tondon

sophia tondon

1620898103

5 Latest Technology Trends of Machine Learning for 2021

Check out the 5 latest technologies of machine learning trends to boost business growth in 2021 by considering the best version of digital development tools. It is the right time to accelerate user experience by bringing advancement in their lifestyle.

#machinelearningapps #machinelearningdevelopers #machinelearningexpert #machinelearningexperts #expertmachinelearningservices #topmachinelearningcompanies #machinelearningdevelopmentcompany

Visit Blog- https://www.xplace.com/article/8743

#machine learning companies #top machine learning companies #machine learning development company #expert machine learning services #machine learning experts #machine learning expert

Jackson  Crist

Jackson Crist

1617331066

Intro to Reinforcement Learning: Temporal Difference Learning, SARSA Vs. Q-learning

Reinforcement learning (RL) is surely a rising field, with the huge influence from the performance of AlphaZero (the best chess engine as of now). RL is a subfield of machine learning that teaches agents to perform in an environment to maximize rewards overtime.

Among RL’s model-free methods is temporal difference (TD) learning, with SARSA and Q-learning (QL) being two of the most used algorithms. I chose to explore SARSA and QL to highlight a subtle difference between on-policy learning and off-learning, which we will discuss later in the post.

This post assumes you have basic knowledge of the agent, environment, action, and rewards within RL’s scope. A brief introduction can be found here.

The outline of this post include:

  • Temporal difference learning (TD learning)
  • Parameters
  • QL & SARSA
  • Comparison
  • Implementation
  • Conclusion

We will compare these two algorithms via the CartPole game implementation. This post’s code can be found here :QL code ,SARSA code , and the fully functioning code . (the fully-functioning code has both algorithms implemented and trained on cart pole game)

The TD learning will be a bit mathematical, but feel free to skim through and jump directly to QL and SARSA.

#reinforcement-learning #artificial-intelligence #machine-learning #deep-learning #learning

E-learning Software Services - SISGAIN

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