ABOUT US

Edge Intelligence is a research program of the MIAI institute that promotes locally distributed computations related to AI to avoid the waste of energy lost during data transfers.
Thanks to a small data center close to the edge of the network, data produced on mobile devices can be pre-computed in low latency networks. Further computations maybe then done on a regular data center over the Cloud.
Study new methods for distributed/federated Machine Learning.
Develop new models and mechanisms for efficient managing of local resources at the edge.

FIELDS OF INTEREST

Distributed learning

Distribute the computation for better efficiency. (E.g. computing power, workload, storage management)

Federated learning

Collaborate with the intent to keep the data of each agent heterogeneous, local, and private. Federated learning can be centralized, decentralized, and semi-centralized.

Task management

Efficient allocation and execution of the tasks in the appropriate heterogeneous and dynamic distributed computing devices connected at the edge level.

Online learning

Predict better for future data. In online settings, data becomes available in sequential order.

THE TEAM

Board

Denis Trystram

- Optimization - Distributed scheduling -

Thierry Coupaye

- Distributed scheduling - IoT -

Frédéric Desprez

- FL - Ressource management -

Yiannis Georgiou

- Infrastructure for the edge -

Thang Nguyen

- Optimization - FL -

Academic staff

Noel de Palma

- System for edge computing -

Didier Donsez

- IoT -

Jean-Paul Jamont

- Multi-agent AI -

Philippe Lalanda

- Pervasive FL -

Grégory Mounié

- Scheduling -

François Portet

- Pervasive FL -

Thomas Ropars

- System for edge computing -

PHD / Post Docs

Tuan Anh Nguyen

- MIAI -

Abdessalam Benhari

- Atos -

Danilo Carastan-Santos

- MIAI -

Anderson Andrei Da Silva

- Ryax -

Sannara Ek

- Naval group -

Amal Guittoum

- Orange Labs -

Mathilde Jay

- MIAI -

Nihel Kaboubi

- Orange Labs -

Enikő Kevi

- MIAI -

Closson Louis

- Adeunis -

Angan Mitra

- Qarnot -

Hamza Safri

- Berger-Levrault -

Paul Youssef

- MIAI -

Companies

Adeunis

ATOS-Bull

Berger Levrault

Carbon Bee

Naval group

Orange

Qarnot

RYAX

Flowers in Chania
Flowers in Chania
Flowers in Chania
Flowers in Chania
Flowers in Chania
Flowers in Chania
Flowers in Chania
Flowers in Chania
Flowers in Chania
Flowers in Chania

MEETINGS

Date Title
3rd July 2019Kick-off meeting
10th December 2019Federated Learning
11th February 2020Hardware and software aspects of edge computing
28th January 2021The various forms of distributed learning
16th March 2021Edge infrastructure
8th June 2021Low cost machine learning
6th October 2021Internet Of Things and learning
10th December 2021IoT Frameworks and Neural Network at the Edge
27th October 2022Frugal federated learning

PRODUCTIONS

Date Title Conference
December 2022One Gradient Frank-Wolfe for Decentralized Online Convex and Submodular OptimizationACML2022
November 2022A stochastic conditional gradient algorithm for decentralized online convex optimizationJPDC
June 2022Towards Developing a Global Federated Learning Platform for IoTICDCS2022
June 2022Online Decentralized Frank-Wolfe: From Theoretical Bound to Applications in Smart-BuildingGIoTS2022
May 2022A Federated Learning Framework for IoT: Application to Industry 4.0CCGRID2022
April 2022Two-Agent Scheduling with Resource Augmentation on Multiple Machines EUROPAR2022
March 2022Scheduling on Two Types of Resources: A Survey.ACM Computing Survey
February 2022Improving the Performance of Batch Schedulers Using Online Job Runtime ClassificationIEEE TPDS
September 2021Short-Term Ambient Temperature Forecasting for Smart HeatersISCC2021
August 2021Smart Oracle Based Building Management SystemSMARTCOMP