Foundation models (FMs) adapt surprisingly well to downstream tasks with fine-tuning.
However, their colossal parameter space prohibits their training on resource-constrained …

The rapid development of AI systems has been greatly influenced by the emergence of
foundation models. A common approach for targeted problems involves fine-tuning these …

Existing federated learning methods have effectively dealt with decentralized learning in
scenarios involving data privacy and non-IID data. However, in real-world situations, each …

Federated Learning has emerged as a revolutionary technology in Machine Learning (ML),
enabling collaborative training of models in a distributed environment while ensuring privacy …

Large foundation models (FMs) adapt surprisingly well to specific domains or tasks with fine-
tuning. Federated learning (FL) further enables private FM fine-tuning using the local data …

In federated learning, data heterogeneity significantly impacts performance. A typical
solution involves segregating these parameters into shared and personalized components …

In federated learning scenarios, data heterogeneity can significantly impact performance.
Personalized federated learning seeks to provide individualized models for each client to …

This document is a summary of some of the research I conducted from 2018 to 2023 to
obtain the Habilitation à Diriger des Recherches. All the results mentioned are discussed in …

Federated Learning is an emerging machine learning technology proposed by Google in
2016, which allows for collaborative training across multiple devices without the need to …

Current Federated Learning (FL) methods often struggle with high statistical heterogeneity
across clients' data, resulting in client drift due to biased local solutions. This issue is …