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Javier Fernandez-Marques

4 accepted papers

2025

FlowerTune: A Cross-Domain Benchmark for Federated Fine-Tuning of Large Language Models

NeurIPS 2025poster

Large Language Models (LLMs) have achieved state-of-the-art results across diverse domains, yet their development remains reliant on vast amounts of publicly available data, raising concerns about data scarcity and the lack of access to domain-specific, sensitive information. Federated Learning (FL)…

Cited by 0SourceScholar
2024

Recurrent Early Exits for Federated Learning with Heterogeneous Clients

ICML 2024poster

Federated learning (FL) has enabled distributed learning of a model across multiple clients in a privacy-preserving manner. One of the main challenges of FL is to accommodate clients with varying hardware capacities; clients have differing compute and memory requirements. To tackle this challenge, r…

2022

ZeroFL: Efficient On-Device Training for Federated Learning with Local Sparsity

ICLR 2022poster

When the available hardware cannot meet the memory and compute requirements to efficiently train high performing machine learning models, a compromise in either the training quality or the model complexity is needed. In Federated Learning (FL), nodes are orders of magnitude more constrained than tra…

Cited by 77SourcePDFScholar
2021

Degree-Quant: Quantization-Aware Training for Graph Neural Networks

ICLR 2021poster

Graph neural networks (GNNs) have demonstrated strong performance on a wide variety of tasks due to their ability to model non-uniform structured data. Despite their promise, there exists little research exploring methods to make them more efficient at inference time. In this work, we explore the vi…

Cited by 219SourcePDFScholar