Practical Techniques for Federated Architecture - IT Assignment Help

Assignment Task

Abstract: In the traditional Machine Learning methods, sensitive user data is sent to central servers where models are trained. In the long term, a centralized model like this is bad for society and the market because it can lead to monopolization by a few powerful players. Modern mobile applications have access to a plethora of data that can be used to train learning models.

They contain rich data which are often privacy sensitive impeding it to be directly used in a data center using conventional approaches. The solution to this problem is a new approach in which data is trained on mobile devices and the global model is trained by aggregating local models. This decentralized approach is termed Federated Learning which introduces a practical approach for federated neural networks learning based on iterative model averaging.

Federated learning is a machine learning paradigm in which multiple mobile phones collaborate together to train a model under the oversight of a central server while the training information is kept decentralized hence preserving the privacy of a user’s private data. Federated learning is built on the concepts of targeted data processing and it can reduce many of the systemic privacy issues and costs associated with conventional, structured machine learning and data science approaches.

In a Federated learning setting, the aim is to train a global model while keeping the data for training on a large number of clients, each with slow and unreliable connections. This approach considers a training algorithm where on each round, each phone calculates an update to the present model based on its local data and communicates it to a central server, which aggregates the client-side updates to calculate a new global training model. In this setting, communication efficiency is of the utmost importance. However, this form of privacy-preserving collaborative learning comes at the expense of a large amount of data bandwidth.

To tackle this limitation, I will be proposing two novel techniques to reduce bandwidth consumption which are designed specifically to address the needs of Federated Learning. In this paper, I propose techniques based on to reduce the upload bandwidth costs by using structured updates, where we train an update from a constrained space parametrized by a lower set of variables. And compressed updates, wherein we train the full-model update and then compress it before sending it to the server using a combination of encoding and subsampling.

A major anticipated problem with these approaches is that the training algorithm is heuristic, and there is no theoretical assurance that the process is accurate. The application of such an approach would be limited to specific use cases as averaging can be suboptimal for nonconvex problems.

These approaches are based on a limited dataset, and the paper would benefit from a more thorough experimental analysis.

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