Federated learning in automotive industry - Revision history

Slawek at 09:40, 14 October 2023

2023-10-14T09:40:25Z

Zahra at 06:46, 14 October 2023

2023-10-14T06:46:13Z

Zahra: Created page with "{{StudentProjectTemplate |Summary=- |Supervisor=Zahra Taghiyarrenani, Slawomir Nowaczyk, Sepideh Pashami }} With advancements in IoT and edge computing, the automotive industr..."

2023-10-02T14:59:24Z

Created page with "{{StudentProjectTemplate |Summary=- |Supervisor=Zahra Taghiyarrenani, Slawomir Nowaczyk, Sepideh Pashami }} With advancements in IoT and edge computing, the automotive industr..."

New page

{{StudentProjectTemplate
|Summary=-
|Supervisor=Zahra Taghiyarrenani, Slawomir Nowaczyk, Sepideh Pashami
}}
With advancements in IoT and edge computing, the automotive industry needs to begin leveraging the benefits of Machine Learning in a federated setting. Federated Learning (FL) is an approach that allows various clients to collaboratively build Machine Learning (ML) models, transferring small amounts of information and ensuring privacy. As an example, the most well-known FL method is FedAvg, where all clients connect to a server. Every client trains a model and sends the model to the server. The server calculates the average of all models as a global model, and sends it back to clients. This procedure will continue until the convergence of the global model.

However, implementing a FL approach for the automotive industry faces many challenges. Some are related to the infrastructure -- for example, there are scenarios where sensors logging the data might be partially different, requiring Federated Transfer Learning. It is possible that the sensors are the same, but they operate on different schedules, requiring Asynchronous Federated Learning. It is also possible that the clients operate in different environments such that the factors influencing the decisions differ, which requires Heterogeneous Federated Learning. Moreover, depending on how much labeled and unlabelled data each client owns, one can consider supervised, semi-supervised, self-supervised, etc., setups.

As there are various types of challenges, the MSc thesis can start open-ended, and the topic(s) of most relevance for Federated Learning in the Automotive Industry can be explored.

The initial thesis plan:
1. A comprehensive analysis of the Challenges, Solutions, and Future of FL in the Automotive Industry. It is possible for a high-quality survey paper to be produced as a result of working on this topic.

2. Predictive maintenance can benefit the industry by minimizing the costs and risks associated with maintenance. Predictive maintenance models, such as the Remaining Useful Lifetime (RUL) predictor, are of interest in federated settings. Scientifically, this topic is related to Federated Learning for regression, specifically in heterogeneous situations (system heterogeneity or statistical heterogeneity), which is understudied.

3. Detecting anomalies is a critical task in predictive maintenance, safety, efficiency, etc. -- and, consequently, in the automotive industry. Thus, Federated Anomaly Detection is another potential area of study.

4. Data availability, specifically when it comes to solving real-world tasks, is a challenge. Semi-supervised and Self-supervised Federated Learning has shown promising results and should be explored in the automotive context.

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 {{StudentProjectTemplate
 |Summary=Federated learning in automotive industry
 |Supervisor=Zahra Taghiyarrenani, Slawomir Nowaczyk, Sepideh Pashami
 }}
 With advancements in IoT and edge computing, the automotive industry needs to begin leveraging the benefits of Machine Learning in a federated setting. Federated Learning (FL) is an approach that allows various clients to collaboratively build Machine Learning (ML) models, transferring small amounts of information and ensuring privacy. As an example, the most well-known FL method is FedAvg, where all clients connect to a server. Every client trains a model and sends the model to the server. The server calculates the average of all models as a global model, and sends it back to clients. This procedure will continue until the convergence of the global model.