LearnNet

While machine learning is revolutionizing entire sectors of the digital economy and scientific research, its robust deployment in digital infrastructures raises many questions.

The challenge Learning Networks (LearnNet) explores new avenues of research at the intersection of the fields of networks and learning.

This challenge has two complementary objectives: rethinking the design of network protocols to serve machine learning applications, and exploring how learning can improve network management.

Thus the LearnNet challenge studies the growing entanglement between the challenges of large-scale learning and network design.

Heterogeneity in the context of distributed learning is a key element: heterogeneity of architectures, data and learning objectives.

The project involves 8 Inria teams (COATI, EPIONE, MARACAS, NEO, PREMEDICAL, STATIFY, TOTH, TRIBE) providing advanced expertise in networks, optimization and decentralized or federated learning.

Interactions are planned with the Fedmalin challenge and the PEPR-NF.