Introduction to Federated Learning and Challenges

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.

#machine-learning #federated-learning #ai #data-science #privacy-preserving

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Introduction to Federated Learning and Challenges

Introduction to Federated Learning and Challenges

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.

#machine-learning #federated-learning #ai #data-science #privacy-preserving

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.

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

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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

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