Ph.D. student position in privacy-preserving federated learning

Federated learning is revolutionizing the way in which machine learning models are trained and deployed. It holds great promise in unlocking the full potential of AI in various domains, including healthcare, finance, autonomous driving, smart cities, and satellite networks. Joining our ambitious and dynamic team focused on federated learning will provide you with the opportunity to work with a wide network of international collaborators, enabling you to make a significant impact in this rapidly-evolving field.

Join our team and lead pioneering research endeavors aimed at ensuring the privacy and security of federated learning!

Project abstract
Traditional machine learning methods rely on centralizing data in the cloud, often raising concerns about compromising user privacy and security. In response to these critical challenges, federated learning has emerged as a groundbreaking paradigm shift. Instead of centralizing raw data, federated learning prioritizes sharing knowledge (represented by trained models or gradients) while preserving the integrity of sensitive information. However, the shared models/gradients are susceptible to information extraction attacks that jeopardize the privacy of the users. Further, federated learning is also vulnerable to poisoning attacks where clients may deviate from consensus by steering the global model toward their own objectives.

The successful adoption of federated learning in sensitive domains like healthcare and finance hinges on effectively addressing these hurdles. The core objective of this research project is to provide a fundamental understanding of the privacy and security of federated learning under practical considerations such as regularization, quantization, model sparsification, and accounting for inherent client heterogeneity.

This position is funded by the Swedish Research Council with a "project grant for research into cyber and information security."

The selected candidate will be supervised by Prof. Alexandre Graell i Amat and Dr. Khac-Hoang Ngo at the Department of Electrical Engineering and will integrate an ambitious and dynamic team on decentralized learning currently consisting of 2 Ph.D. students and 2 postdocs. The hired student will join a vibrant and internationally renowned research team at Chalmers, conducting research spanning many fields and including theoretical machine learning, information and coding theory, and signal processing.

The selected candidate will also enjoy our close collaboration with AI Sweden, the Swedish national center for applied artificial intelligence, and will be co-supervised by Dr. Johan Östman at AI Sweden. The student will further take advantage of our current collaborations on decentralized learning with the Technical University of Munich (Germany) and Aalto University (Finland).

Major responsibilities
In this project, you will lead and perform cutting-edge research on privacy- and security-preserving federated learning. You are expected to publish high-quality high-impact papers, work with other PhD students and senior researchers in the team.

Qualifications
To qualify for the position, you must have a master's level degree in Mathematics, Theoretical Physics, Computer Science, Electrical Engineering, or similar, corresponding to at least 240 higher education credits.
Applicants must have a strong background in mathematics. Knowlege about machine learning is an asset.

We look for candidates with a strong interest in pursuing theoretical research, who are independent, curious, and creative, and have the ability to work in an international environment and to present their ideas effectively.

Good programming skills (Python) are required.

The position requires sound verbal and written communication skills in English. 

Contract terms
The position is a full time temporary employment for 4 years. During the Ph.D., the student is also expected to teach for the equivalent of around 6 months, and the contract is extended accordingly (i.e., the total duration is around 4.5 years). You will receive a competitive salary (the starting gross salary is 33500 SEK/month and it increases during the duration of the Ph.D.) with healthcare, social benefits, and pension, and enjoy students' benefits (such as student housing).

Information about the department
At the Department of Electrical Engineering, we conduct internationally renowned research in artificial intelligence, information and communication theory, biomedical engineering, signal processing, and image analysis. We offer a dynamic and international research environment with about 300 employees from more than 20 countries, and with extensive national and international research collaborations with academia, industry and society. 

We offer
Chalmers offers a cultivating and inspiring working environment in the coastal city of Gothenburg. 
Read more about working at Chalmers and our benefits for employees.

Chalmers aims to actively improve our gender balance. We work broadly with equality projects, for example the GENIE Initiative on gender equality for excellence. Equality and diversity are substantial foundations in all activities at Chalmers.

Application procedure
The application should be marked with ref. number 20230749 and written in English. The application should be sent electronically and be attached as PDF-files, as below. Maximum size for each file is 40 MB. Please note that the system does not support Zip files.

CV: (Please name the document: CV, Family name, reference number)
• CV
• Other, for example previous employments or leadership qualifications and positions of trust.
• Two references that we can contact.

Personal letter: (Please name the document as: Personal letter, Family name, ref.number)
1-3 pages where you:
• Introduce yourself
• Describe your previous experience of relevance for the position (e.g. education, thesis work and, if applicable, any other research activities)
• Describe your future goals and future research focus

Other documents:
• Copies of bachelor and/or master’s thesis.
• Attested copies and transcripts of completed education, grades and other certificates, e.g. TOEFL test results.

Application deadline: 31 January, 2024



For questions, please contact:
Alexandre Graell i Amat, Department of Electrical Engineering, alexandre.graell@chalmers.se