2022-05178 - PhD Position F/M Distributed Machine Learning in Ubiquitous Environments using Location-dependent Models
Contract type : Fixed-term contract
Level of qualifications required : Graduate degree or equivalent
Fonction : PhD Position
Level of experience : Recently graduatedAbout the research centre or Inria department
The Inria Rennes - Bretagne Atlantique Centre is one of Inria's eight centres and has more than thirty research teams. The Inria Center is a major and recognized player in the field of digital sciences. It is at the heart of a rich R&D and innovation ecosystem: highly innovative PMEs, large industrial groups, competitiveness clusters, research and higher education players, laboratories of excellence, technological research institute, etc.Context
Context & Funding
The proposed Ph.D. will take place within the Fed-Malin Inria Challenge project. Fed-Malin aims to address the methodological challenges of moving ML operations from the comfortable cloud nest to the wild Internet. Most existing research considers the “Google/Apple setting” with a large set of relatively homogeneous smartphones and the cloud. By contrast, we will consider various scenarios, including entities with significant computation resources (e.g., companies, hospitals), edge servers deployed by telecommunications operators, and (potentially heterogeneous) IoT devices with or without AI edge accelerators.
Fed-Malin will shed light on the design principles of distributed systems for ML. It will help to better configure the existing ones, as well as to conceive the next generation. More efficient distributed learning systems can reduce cost barriers to access AI technology and mitigate the present concentration of power at the few technology giants that can afford massive computing power. They may also operate under lower energy budgets and thus potentially contribute to making AI more sustainable.
Supervision and Location
The Ph.D. will be co-supervised by the Inria teams WIDE (the World Is DistributEd, in Rennes) and Spirals (Self-adaptation for distributed services and large software systems, in Lille). The successful candidate might be located in either Lille or Rennes (France).
Complete Ph.D. topic description available here.Assignment
In many applications, machine learning models are intrinsically tailored to a given geographical area. This is the case, for example, of smart building management 4, crowdsensing for environmental monitoring 9, and smart wireless transmission techniques 10. These models benefit from continuous data streams generated by sensors and/or mobile devices. The goal of this Ph.D. is to design, deploy and characterize decentralized learning algorithms and frameworks that can preserve the privacy of their users while delivering location-dependent services.
Research Questions & Work Plan
More specifically, our objective in this Ph.D. project is to investigate how decentralized machine learning can be effectively deployed on mobile devices to design and implement location-dependent applications accessible to end- users in the field while preserving their privacy. This objective calls for a combination of novel algorithms, protocols, and middleware solutions. More specifically, we foresee that achieving this objective requires addressing three challenges:
As a matter of demonstration and assessment of the contributions to the above challenges, we intend to consider two case studies in the area of mobile crowdsourcing software systems:
The above crowdsensing case studies will draw upon the expertise gathered within the Spirals team on crowdsensing platforms, particularly through the APISENSE 9 online platform. The Ph.D. might also involve experiments on specialized testbeds, such as FIT IoT-LAB, which can be used to collect IoT data and experiment with actual ML and FL implementations.Main activities
Perform scholarly research in algorithmic and distributed systems. This includes but is not limited to :
monthly gross salary amounting to 1982 euros for the first and second years and 2085 euros for the third yearGeneral Information
Theme/Domain : Distributed Systems and middleware System & Networks (BAP E)
Town/city : Rennes ou Lille
The candidate recruited for this Ph.D. should have a Master's Degree in Computer Science or equivalent, with a solid algorithmic and systems background, particularly regarding at least one of the following: distributed computer systems, machine learning, and/or mobile computing. Good programming skills and a willingness to learn about new techniques (decentralized machine learning and privacy protection) are also crucial, as well as good writing skills and the ability to propose, present, and discuss new ideas in a collaborative setting.About Inria
Inria is the French national research institute dedicated to digital science and technology. It employs 2,600 people. Its 200 agile project teams, generally run jointly with academic partners, include more than 3,500 scientists and engineers working to meet the challenges of digital technology, often at the interface with other disciplines. The Institute also employs numerous talents in over forty different professions. 900 research support staff contribute to the preparation and development of scientific and entrepreneurial projects that have a worldwide impact.Instruction to apply
Please submit online : your resume, cover letter and letters of recommendation eventually
For more information, please contact [email protected]
Defence Security : This position is likely to be situated in a restricted area (ZRR), as defined in Decree No. 2011-1425 relating to the protection of national scientific and technical potential (PPST).Authorisation to enter an area is granted by the director of the unit, following a favourable Ministerial decision, as defined in the decree of 3 July 2012 relating to the PPST. An unfavourable Ministerial decision in respect of a position situated in a ZRR would result in the cancellation of the appointment.
Recruitment Policy : As part of its diversity policy, all Inria positions are accessible to people with disabilities.
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