Internship | Advancing federated learning for defense applications

Introduction:
In conventional training of machine learning models, data is stored in one location. More data often means better performance, thus if multiple parties want to contribute to the training, they have to share their data. Nevertheless, when data is sensitive, data sharing is often not possible nor allowed. This is the case in defense applications, where AI models have to be trained on sensitive military data, which cannot be shared across different countries or organizations.
Federated Learning (FL) is a technique whereby an AI model can be collaboratively trained with data from multiple parties without having to upload this data to a central location. This technique has proven to be very effective and more secure than conventional training. Nevertheless, training in a FL setup has two downsides: it is slow due to the communication overhead and it is difficult to perform well on realistic data distributions.

Research on federated learning often focusses on situations where all participating parties in the FL setup have similar private datasets (“independent and identically distributed” a.k.a. “IID”), for example where the number of samples and classes in each client are uniform, and the images in each individual dataset are similar.
In real-world scenarios, this is often not the case. Here, data can be very unbalanced (“non-IID”). Research has shown that non-IID data can result in a performance drop, raise security risks, and increase the training time even more compared to training on IID data.

The aim of the thesis is to make FL work in a realistic scenario. For this, three objectives are defined:

• Investigate how to create datasets in each FL client that realistically mimic a possible real-world scenario.
• Explore strategies to account for the negative effects of a realistic non-IID data distribution.
• Investigate how training on realistic non-IID data can be more efficient, for example by applying less communication steps.

What will be your role?
Your role will be to explore the types and the effects of non-IID data on FL, and investigate the state-of-the-art strategies to account for effects of different types of non-IID data. You will start with a literature study on the types of non-IID data, and the effects that the different types of non-IID data have on the training of the model. It will especially be interesting to gain knowledge on the effects of a combination of non-IID data distribution types, since the more types that are combined, the more “realistic” the dataset becomes. Your research could make a direct impact in research on federated learning for defense applications, where the goal is t

Your research could make a direct impact in the development of automatic threat detection systems using federated learning, where the goal is to identify and mitigate potential security risks. The proposed solution can enhance the accuracy and security of defense applications while also being more robust to real-world data distributions. This allows multiple defense organizations to collaboratively develop models without sharing sensitive data, thereby streamlining processes and protecting classified information.

You will perform this assignment within TNO’s Intelligent Imaging department. The Intelligent Imaging department is a passionate, creative, and dedicated team of professionals (60 people) specializing in developing groundbreaking applications in the field of computer vision. Our team members have diverse backgrounds, ranging from the medical field to artificial intelligence. Intelligent Imaging is a young and growing department that has built up a lot of expertise over the past years in AI and deep learning.

We are looking for a master’s student who wants to join our cutting-edge research team to explore the boundaries of training FL models for computer vision tasks. This position is perfect for students who are passionate about AI, computer vision, and advanced machine learning techniques. You should be interested in taking a deep dive into the world of non-IID data in FL and helping us uncover how to handle training FL setups on datasets that mimic realistic real-world data distributions.

Additional requirements include being in the final stages of your master's degree in artificial intelligence, computer science, physics, mathematics, electrical engineering, or a similar field. You should have some experience in computer vision, artificial intelligence, deep learning and Python programming. Experience with federated learning or handling of non-IID data is not required, but basic understanding of training an AI model is.

Please include the following practical information in your cover letter:

• whether you are looking for an internship or a graduation project,
• the preferred duration of your project (flexible in the range of 6 to 12 months),
• your desired start date (flexible, but due to the required security screening the earliest possible start date is three months after submitting your application).

You want an internship opportunity on the precursor of your career; an internship gives you an opportunity to take a good look at your prospective future employer. TNO goes a step further. It’s not just looking that interests us; you and your knowledge are essential to our innovation. That’s why we attach a great deal of value to your personal and professional development. You will, of course, be properly supervised during your work placement and be given the scope for you to get the best out of yourself. Furthermore, we provide:

• A highly professional, innovative internship environment, within a team of top experts.
• A suitable internship allowance (615 euro for wo-, hbo- and mbo-students, for a full-time internship).
• Possibility of eight hours of free leave per internship month (for a full-time internship).
• A free membership of Jong TNO, where you can meet other TNO professionals and join several activities, such as sports activities, (work-related) courses or the yearly ski-trip.
• Use of a laptop.
• An allowance for travel expenses in case you don’t receive an OV-card.

At TNO, we innovate for a healthier, safer and more sustainable life. And for a strong economy. Since 1932, we have been making knowledge and technology available for the common good. We find each other in wonder and ingenuity. We are driven to push boundaries. There is all the space and support for your talent and ambition. You work with people who will challenge you: who inspire you and want to learn from you. Our state-of-the-art facilities are there to realize your vision. What you do at TNO matters: impact makes the difference. Because with every innovation you contribute to tomorrow’s world. Read more about TNO as an employer.

At TNO we encourage an inclusive work environment, where you can be yourself. Whatever your story and whatever unique qualities you bring to the table. It is by combining our unique strengths and perspectives that we are able to develop innovations that make a real difference in society. Want to know more? Read what steps we are taking in the area of diversity and inclusion.

After the first CV selection, the application process will be conducted by the concerning department. TNO will provide a suitable internship agreement. If you have any questions about this vacancy, you can contact the contact person mentioned below.

For this internship vacancy it is required that the AIVD issues a security clearance (VGB) after conducting a security screening. Take into account that this process may take about 8 weeks. If you have been abroad for more than 6 consecutive months, or if you do not have the Dutch nationality, it may take longer. Read more about security screening on the AIVD website.

Important to be aware of before applying:

• Before the start of the internship, the internship agreement from TNO needs to be signed. For students at a college or university based in the Netherlands, TNO uses the UNL-template (supplemented with a number of specific agreements from TNO). For students of foreign and MBO educational institutions, the TNO internship agreement applies. TNO does not sign any other internship agreements.
• Before the start of the internship, the educational institution will need to confirm in writing that:
  • 1) you are enrolled at the educational institution during the internship, and;
  • 2) the internship takes place as part of the programme of the study.
• The confirmation of educational institution takes place by signing the UNL template or forms prepared by TNO.
• Interns at TNO must be in possession of a Dutch residential address at the start of the internship. Performance of internship activities from abroad is not possible.