Offre en lien avec l’Action/le Réseau : – — –/– — –
Laboratoire/Entreprise : Orange Lab / UCA-Inria-CNRS MAASAI Team
Durée : 36 months
Contact : firstname.lastname@example.org
Date limite de publication : 2022-03-14
TThe goal of the thesis is to perform research on decentralized and efficient federated AutoML learning for heterogeneous embedded devices.
The training of AI models for service delivery is today facing a conceptual transformation, by shifting the learning of models close to the data, embedded on users’ devices. These devices have limited resources and must remain fully operational during the learning phase. In addition, users today generate sensitive data and new collaborative algorithms for learning need to be developed and optimized for different embedded devices, ranging from smartphones to IoT.
Nowadays, to build an AI model it is necessary to collect data on a central server (cloud). The problems of this method are related to privacy, control of data usage and computational resources. Federated learning (FL) [1,2] is a new AI approach with collaborative training that resolves these problems. Models are trained on local users’ data and its parameters only are exchanged with other users to build a global model. The challenges of Federated Learning are (a) obtaining efficient and robust decentralized FL models with heterogeneous data (b) optimizing resources for actual operational deployment and (c) customizing services and optimizing model based on available resources for groups of users, because a single global model may be less explainable, accurate and appropriate when compared to a personalized model.
We will deploy deep neural networks on users’ devices because they have high classification/prediction accuracy in various tasks. However, their training requires a significant effort in terms of finding optimal hyperparameters, which limits their use at devices with constrained resources. Emerging areas address the problem of automatic neural network generation  and automatic search for appropriate architectures (Neural Architecture Search-NAS), features required for real-world deployments. FL NAS  aims at optimizing the architecture of neural network models in the FL environment. Many questions in this domain remain open. For example, there are no approaches developed for FL with clients having the same sample space and a different feature space.
The objective of the thesis is to (a) design a federated learning framework to automatically generate low-power neural networks in compliance with GDPR  with homogeneous (b) and heterogeneous devices under device constraints (availability, resources, states) and to study it in a fully decentralized Peer-to-Peer federated learning setup.
 J. Konecny, H. B. McMahan, F. X. Yu, P. Richtarik, A. T. Suresh and D. Bacon, “Federated Learning: Strategies for Improving Communication Efficiency,” in arXiv, 2017, pp. 1-10.
 K. Bonawitz, H. Eichner, W. Grieskamp, D. Huba, A. Ingerman, V. Ivanov, Ch. M. Kiddon, J. Konečný, S. Mazzocchi, B. McMahan, T. Van Overveldt, D. Petrou, D. Ramage and J. Roselander, “Towards Federated Learning at Scale: System Design,” SysML 2019, https://arxiv.org/abs/1902.01046, 2019.
 A. Wong, M. J. Shafiee, B. Chwyl and F. Li, “FermiNets: Learning generative machines to generate efficient neural networks via generative synthesis,” 1809.05989.pdf (arxiv.org), NIPS, 2018.
 H. Zhu, H. Zhang and Y. Jin, “From Federated Learning to Federated Neural Architecture Search,” https://arxiv.org/pdf/2009.05868.pdf, 2020.
 Regulation (EU) 2016/679 of the European Parliament and of the Council (article 30), https://eur-lex.europa.eu/legal-content/EN/TXT/HTML/?uri=CELEX:32016R0679&from=EN#d1e3265-1-1, Archived from the original on 28 June 2017.
Profil du candidat :
You have a Masters degree in Data Science or Computer Science and you are a curious person that likes to learn and seek for solutions. You are highly motivated to do your thesis in the emerging field of distributed algorithms for embedded devices. You have skills in machine learning, optimization and statistics (essential) as well as good programming skills and knowledge in the field of embedded devices (desirable). Interest in the field of Signal Processing is a plus.
Furthermore, autonomy and open-mindedness are the qualities particularly appreciated for research work. The dynamism, the strength of proposal and the capacities of communication are also required for this position. English will be used throughout the thesis (reading state of the art, writing articles and presenting results at international conferences) and excellent level of English is therefore required.
Formation et compétences requises :
Master or Ecole d’ingénieur.
Adresse d’emploi :
Orange Lab Sophia Antipolis.