A study on performance limitations in Federated Learning

This Increasing privacy concerns and unrestricted access to data communication overhead slows down the convergence of lead to the development of a novel machine learning the Machine Learning algorithms. For example, the client paradigm called Federated Learning (FL). FL borrows many devices could be self-driving cars in which the goal might be of the ideas from distributed machine learning, however, the to create a driver sleep prevention face recognition machine challenges associated with federated learning makes it an learning system preventing road accidents or making use of interesting engineering problem since the models are trained large volumes of traffic training data from cameras in the on edge devices. It was introduced in 2016 by Google, and vehicles to improve the vehicle AI agent's driving since then active research is being carried out in different capability. Because in both cases, due to the possibility of areas within FL such as federated optimization algorithms, collecting large number of samples by increasing the client model and update compression, differential privacy, devices, the data used to train models will have a large robustness, and attacks, federated GANs and privacy variance (carries more Information) and will be more robust preserved personalization. There are many open challenges to bias (race of the driver, different types of roads, and in the development of such federated machine learning pedestrian scenarios) and thus underrepresentation of systems and this project will be focusing on the samples is minimized. The slower client connections might communication bottleneck and data Non IID-ness, and its also cause stragglers.