Salle 104 du bâtiment 11 dit le château, 2 Place Pierre Viala Campus La Gaillarde

Le séminaire commencera par un café offert par les Halles de l'IA. Au programme :
(1) News du W3C en 10 minutes, par Pierre-Antoine CHAMPIN, W3C / INRIA / Université de Lyon
(2) Propagation de propriétés entre entités contextuellement identiques par Pierre Henri PARIS, Universite Paris-Saclay :

Inria Montpellier, St-Priest Campus, Building 5, Room 02/022
Machine Learning in Montpellier, Theory & Practice

In decentralized machine learning, different devices communicate in a peer-to-peer manner to collaboratively learn from each other's data. Such approaches are vulnerable to misbehaving (or Byzantine) devices. We introduce F-RG, a general framework for building robust decentralized algorithms with guarantees arising from robust-sum-like aggregation rules F. We then investigate the notion of breakdown point, and show an upper bound on the number of adversaries that decentralized algorithms can tolerate. We introduce a practical robust aggregation rule, coined CSours, such that CSours-RG has a near-optimal breakdown. Other choices of aggregation rules lead to existing algorithms such as ClippedGossip or NNA. We give experimental evidence to validate the effectiveness of CSours-RG and highlight the gap with NNA, in particular against a novel attack tailored to decentralized communications.

MachineLearning, LabéliséHallesIA, IA&Expert

Machine Learning in Montpellier, Theory & Practice - Pedro Valdeira

Vertical federated learning trains models from feature-partitioned datasets across multiple clients, who collaborate without sharing their local data. Standard approaches assume that all feature partitions are available during both training and inference. Yet, in practice, this assumption rarely holds, as for many samples only a subset of the clients observe their partition. However, not utilizing incomplete samples during training harms generalization, and not supporting them during inference limits the utility of the model.

Vertical Federated Learning, Missing Feature Blocks, LabéliséHallesIA

Salle 104 du bâtiment 11 dit le château, 2 Place Pierre Viala Campus La Gaillarde, 34000 Montpellier
SEmantic web SeminAr MontpEllier

Le séminaire commencera par un café offert par les Halles de l'IA. Au programme :
(1) News du W3C en 10 minutes, par Pierre-Antoine CHAMPIN, W3C / INRIA / Université de Lyon
(2) FAIRiser les données du réseau d’observatoires de la zone critique Français (OZCAR) : cas d’utilisation de l’ontologie I-ADOPT par Charly COUSSOT, IRD Université Grenoble Auvergne Rhône Alpes

LabéliséHallesIA