PhD on Protecting cloud-edge continuum against privacy and robustness threats (M/F)
New
- FTC PhD student / Offer for thesis
- 36 mounth
- Doctorate
This offer is available in English version
This offer is open to people with a document recognizing their status as a disabled worker.
Offer at a glance
The Unit
Laboratoire d'informatique en image et systèmes d'information
Contract Type
FTC PhD student / Offer for thesis
Working hHours
Full Time
Workplace
VILLEURBANNE ()
Contract Duration
36 mounth
Date of Hire
20/04/2026
Remuneration
2300 € gross monthly
Apply Application Deadline : 12 March 2026 23:59
Job Description
Thesis Subject
Federated learning (FL) is a promising paradigm that is gaining grip in the context of privacy-preserving machine learning for edge computing systems. Thanks to FL, several data owners called clients (e.g., organizations in cross-silo FL) can collaboratively train a model on their private data, without having to send their raw data to external service providers. FL was rapidly adopted in several thriving applications such as digital healthcare, that is generating the world's largest volume of data. Decentralized Learning (DL) goes further by providing serverless federated learning, where the data are kept at the clients and no server is needed. Thus, DL involves distributed and decentralized protocols to allow clients to build a global model.
Although DL is a first step towards privacy by keeping the data local to each client, this is not sufficient since the model parameters shared by DL is vulnerable to privacy attack [7], as shown in a line of recent literature [8]. Furthermore, DL is more vulnerable to malicious behaviour from clients that may inject poisoned information in data and models, resulting in a misbehaving and non-robust DL models. Recent studies show that robustness and privacy in DL may compete; handling them independently – as done usually – may have negative side-effects on each other.
Therefore, there is a need for a novel multi-objective approach for FL robustness and protection against privacy threats. This project tackles this challenge and aims to precisely handle the issues raised at the intersection of DL model privacy, robustness and utility, through: (i) Novel DL protocols; (ii) A multi-objective approach to trade-off privacy, robustness and utility, these objectives being antagonistic; (ii) Applying these techniques to DL in edge-cloud continuum systems.
Your Work Environment
This PhD thesis is part of the PEPR Cloud project, specifically the TARANIS project. It will therefore be conducted in collaboration with the other partners of the project.
The doctoral candidate will be affiliated with the LIRIS laboratory and will work within an academic consortium of stakeholders in cloud and edge computing, and distributed AI.
The PhD will be conducted within a environment that fosters co-design and the validation of results in realistic use cases.
It thus offers a stimulating research environment, combining methodological contributions and socio-economic impact.
Compensation and benefits
Compensation
2300 € gross monthly
Annual leave and RTT
44 jours
Remote Working practice and compensation
Pratique et indemnisation du TT
Transport
Prise en charge à 75% du coût et forfait mobilité durable jusqu’à 300€
About the offer
| Offer reference | UMR5205-SARBOU-006 |
|---|---|
| CN Section(s) / Research Area | Information sciences: bases of information technology, calculations, algorithms, representations, uses |
About the CNRS
The CNRS is a major player in fundamental research on a global scale. The CNRS is the only French organization active in all scientific fields. Its unique position as a multi-specialist allows it to bring together different disciplines to address the most important challenges of the contemporary world, in connection with the actors of change.
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