Edge Intelligence is a research program of the MIAI institute that promotes local distributed computations related to AI in order to avoid the waste of energy lost during data transfers.
Thanks to small datacenter close to the edge of the network, data produced on mobile devices can be pre-computed in low latency networks. Further computations may be then done on regular datacenter 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.


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 heterogenous, 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 that are connected at the edge level.

Online learning

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



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 Ahn Nguyen

- MIAI -

Danilo Carastan-Santos

- MIAI -

Anderson Andrei Da Silva

- Ryax -

Sannara Ek

- Naval group -

Vincent Fagnon

- UGA -

Mathilde Jay

- MIAI -

Nihel Kaboubi

- Orange Labs -

Closson Louis

- Adeunis -

Angan Mitra

- Qarnot -

Hamza Safri

- Berger-Levrault -

Mohammed Sana

- CEA-tech -

Paul Youssef

- MIAI -


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