Short-term Research Project on Privacy-preserving / Federated Learning on Mobile / Edge Devices

Fecha: 11 feb. 2024

Ubicación: BARCELONA, ES

Empresa: Telefonica S.A.


Telefonica Research is hiring for a short-term research project focused on Privacy-Preserving / Federated Learning on Mobile/Edge Devices.


Telefonica Research is a leading industrial research lab based in Spain. At Telefonica Research, we follow an open research model in collaboration with Universities and other research institutions, promoting the dissemination of scientific results both through publications in top-tier peer-reviewed international journals and conferences and technology transfer. Our multi-disciplinary and international team counts several full-time researchers, holding PhD degrees in various disciplines of computer science and electrical engineering.


We are looking for a researcher (close to finishing their PhD or in early stages after the PhD) to join our team of experts who are working in the development of privacy-preserving machine learning solutions. During this short-term project, which can last up to 6 months, the researcher will be responsible for designing and implementing privacy preserving federated learning solutions for Android or other mobile/edge platforms, particularly in low resource settings.


Key Responsibilities:

  • Design and develop privacy preserving federated learning solutions for mobile platforms. 
  • Implement state-of-the-art techniques such as differential privacy, secure aggregation, and homomorphic encryption to ensure data privacy.
  • Optimize federated learning algorithms for low resource environments.
  • Collaborate with cross-functional teams to integrate federated learning solutions into existing mobile applications. 
  • Conduct experiments and evaluations to assess the performance of federated learning solutions. 
  • Stay up-to-date with industry trends and developments related to privacy preserving federated learning and make recommendations for future improvements.
  • Write technical reports and documentation to communicate results to internal and external stakeholders.



  • PhD (or close to completion) in Computer Science, Electrical Engineering, or related field.
  • Experience in privacy preserving federated learning in mobile platforms, especially in low resource settings.
  • Strong understanding of machine learning algorithms and their implementation on mobile platforms.
  • Experience with privacy preserving techniques such as differential privacy, secure aggregation, and homomorphic encryption.
  • Strong programming skills in languages such as Java, Kotlin, and Python.
  • Experience with mobile development frameworks such as Android Studio and Xcode.
  • Excellent analytical and problem-solving skills.
  • Strong communication and interpersonal skills to collaborate with cross-functional teams.


#UnidadesGlobales #CDO


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