Fall

Federated learning is a distributed machine learning technique that aggregates every client model on the server side. There can be various types of attacks to destroy the robustness of this learning system. Model poisoning attack is realized after the training is finished. In this project, we will use a recently developed method* for analyzing the effect of model poisoning attack that might occur during training.

We frequently perform multiple activities simultaneously in daily life, such as drinking coffee while walking or conversing with a friend while standing. When the traditional single-label output is preferred for activity recognition with wearable sensor data, this requires multiple classes with many possibilities for concurrent activities, such as creating a class for "drinking coffee while walking" and another class for "talking while walking". Instead, this study will investigate a multi-label output system to provide information about which activities are performed together.

Wearable devices can capture multimodal data corresponding to a person’s activity, stress, and sleep information to measure and improve health and well-being. Besides device measurement, there are also survey data as another information-gathering methodology. These can be related to gold standard questionnaires for sleep and st. Also, it may include some subjective assessments related to health satisfaction, overall health, happiness, diet, etc. In this project, the aim is to apply state-of-the-art deep learning techniques such as CNN, GNN and LSTM.

Engineering programs go through accreditation process by agencies such

Avalanche blockchain provides subnets which are  independent  networks with their own rules for permissioned membership and crypto economics.