Post doctor (2 years) position with focus on secure federated learning

Umeå University is one of Sweden’s largest higher education institutions with over 37,000 students and about 4,700 employees. The University offers a diversity of high-quality education and world-leading research in several fields. Notably, the groundbreaking discovery of the CRISPR-Cas9 gene-editing tool, which was awarded the Nobel Prize in Chemistry, was made here. At Umeå University, everything is close. Our cohesive campuses make it easy to meet, work together and exchange knowledge, which promotes a dynamic and open culture.

The ongoing societal transformation and large green investments in northern Sweden create enormous opportunities and complex challenges. For Umeå University, conducting research about – and in the middle of – a society in transition is key. We also take pride in delivering education to enable regions to expand quickly and sustainably. In fact, the future is made here.

The Department of Computing Science seeks a postdoctoral researcher who will work with attack and defence strategies infederated learning. The employment is full-time for two years with a starting date by agreement.

The department characterized by world-leading research in several scientific fields and a multitude of educations ranked highly in international comparison are now looking for a postdoc within attack and defence strategies in federated learning.    

The department has been growing rapidly in recent years where focus on an inclusive and bottom-up driven environment are key elements in our sustainable growth. Our workplace consists of a diverse group from different nationalities, background and fields. If you work as a postdoctor with us you receive the benefits of support in career development, networking, administrative and technical support functions along with good employment conditions. See more information at:

https://www.umu.se/en/department-of-computing-science/

The successful candidate will contribute to the Autonomous Distributed Systems (ADS) Lab within the Department of Computing Science. The ADS Lab is an internationally leading research group with a focus from distributed AI to autonomous resource management and modern. The Lab currently comprises over 20 experienced and world-leading research colleagues from more than 10 different countries. Collaborations are performed with industries like Google, IBM, Intel, Red Hat, and Ericsson, as well as universities and institutes such as Princeton University, University of Massachusetts Amherst, Carnegie Mellon University, Princeton University, Lawrence Berkeley Lab, Nanyang Technical University in Singapore, Uppsala University, Lund University, Universidad Complutense de Madrid, Leeds University, Barcelona Supercomputer Center, TU Vienna, TU Delft, and many more.  For more information, see https://www.cloudresearch.org

Is this interesting to you? We welcome your application no later than 28 februari 2024.

The rapid development of autonomous systems, connected devices, and distributed applications poses several challenges in dealing with petabytes of data in diverse resource-constrained environments. Federated machine learning (FML) is a collaborative learning solution to handle these problems without sharing data with centralised servers. However, several emerging threats target FML training, learning, and inference to fail or mislead models at early learning rounds, particularly backdoor and bitflip attack and defence strategies under-explored in FML. These results jeopardize achieving trustworthy performance for any downstream tasks. Therefore, this project envisions developing and validating attack and defence strategies in federated learning for limited and diverse non-iid (independent identically distributed) data under non-standard and adversarial settings, which are ideally suited for edge AI infrastructures. These goals can be achieved by inducing unique features in federated learning algorithms such as robust training, model restoration, trustworthy device selection, secure learning and inference, fault-tolerance against failures and attacks, as well as resilient, fair and robust models. The ambition is to validate them in classical non-standard settings and apply them to solutions for constraint environments (e.g., the Internet of Things (IoT) and robotic arms). Potentially, teaching up to a maximum of 20% can be included in the work tasks.

The position is funded by The Knut and Alice Wallenberg Foundation through The Wallenberg AI, Autonomous Systems and Software Program (WASP) as part of the WASP faculty establishment package and has a unique opportunity to work with Cybersecurity Lab, Nanyang Technological University, Singapore. WASP is Sweden’s largest individual research program ever, a major national initiative for strategically motivated basic research, education and faculty recruitment. The program addresses research on artificial intelligence and autonomous systems acting in collaboration with humans, adapting to their environment through sensors, information and knowledge, and forming intelligent systems-of-systems. The vision of WASP is excellent research and competence in artificial intelligence, autonomous systems and software for the benefit of Swedish industry. Read more: https://wasp-sweden.org/

To be appointed under the postdoctoral agreement, the postdoctoral fellow is required to have completed a doctoral degree or a foreign degree deemed equivalent to a doctoral degree. This qualification requirements must be fulfilled no later than at the time of the appointment decision.

To be appointed under the postdoctoral agreement, priority should be given to candidates who completed their doctoral degree, according to what is stipulated in the paragraph above, no later than three years prior. If there are special reasons, candidates who completed their doctoral degree prior to that may also be eligible. Special reasons include absence due to illness, parental leave, appointments of trust in trade union organisations, military service, or similar circumstances, as well as clinical practice or other forms of appointment/assignment relevant to the subject area. Postdoctoral fellows who are to teach or supervise must have taken relevant courses in teaching and learning in higher education.

Candidates are expected to have solid foundations in the theory and algorithms of project-related areas, such as federated machine learning, backdoor attacks and defence strategies for federated learning, distributed systems, and excellent programming ability.

A strong command of both written and spoken English language is a key requirement.

Besides creativity and a curious mind, important personal qualities include the ability to work independently as well as together with others either in a group or outside. You are also expected to have a willingness to develop yourself continuously to become a competent and independent researcher.

Knowledge and experience in federated learning algorithms, distributed algorithms, data-centric optimization, resilient or fault-tolerant distributed learning, security for federated learning, mathematical statistics, edge AI, etc., is desirable.

A full application should include:

• Personal letter that clarifies how your background meets the needs and expectations of this position,
• Curriculum vitae (CV) with publication list,
• Verified copy of doctoral degree certificate or documentation that clarifies when the degree of doctor is expected to be obtained,
• Verified copies of other diplomas, list of completed academic courses and grades,
• Copy of doctoral thesis and up to three relevant articles,
• Other documents that the applicant wishes to claim.

Contact information for reference persons are provided in connection with an eventual interview.

The application must be written in English or Swedish. The application is made through our electronic recruitment system. Documents sent electronically must be in Word or PDF format. Log in to the system and apply via the button at the end of this page. The closing date is 28 februari 2024.

For additional information, please contact: Assist. Prof. Monowar Bhuyan at [email protected] or Prof. Erik Elmroth at [email protected]