20 citations · 64 across the 20 of their papers we have counts for
21 papers
Flower Hub: A Reproducible Benchmarking Platform for Federated Learning in Simulation and Deployment
Yan Gao, Mohammad Naseri, Javier Fernandez-Marques +19
Federated learning (FL) has emerged as a key approach for training models across decentralized data, yet benchmarking in FL remains difficult to reproduce, compare, and extend. Exi…
Building Privacy-and-Security-Focused Federated Learning Infrastructure for Global Multi-Centre Healthcare Research
Fan Zhang, Daniel Kreuter, Javier Fernandez-Marques +10
Collaborative healthcare research across multiple institutions increasingly requires diverse clinical datasets, but cross-border data sharing is strictly constrained by privacy reg…
Mixture of Predefined Experts: Maximizing Data Usage on Vertical Federated Learning
Jon Irureta, Gorka Azkune, Jon Imaz +2
Vertical Federated Learning (VFL) has emerged as a critical paradigm for collaborative model training in privacy-sensitive domains such as finance and healthcare. However, most exi…
FlowerTune: A Cross-Domain Benchmark for Federated Fine-Tuning of Large Language Models
Yan Gao, Massimo Roberto Scamarcia, Javier Fernandez-Marques +18
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, raisin…
Recurrent Early Exits for Federated Learning with Heterogeneous Clients
Royson Lee, Javier Fernandez-Marques, Shell Xu Hu +6
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 clien…
FedMAP: Personalised Federated Learning for Real Large-Scale Healthcare Systems
Fan Zhang, Daniel Kreuter, Carlos Esteve-Yagüe +11
Federated learning (FL) promises to enable collaborative machine learning across healthcare sites whilst preserving data privacy. Practical deployment remains limited by statistica…
How Much Is Hidden in the NAS Benchmarks? Few-Shot Adaptation of a NAS Predictor
Hrushikesh Loya, Łukasz Dudziak, Abhinav Mehrotra +4
Neural architecture search has proven to be a powerful approach to designing and refining neural networks, often boosting their performance and efficiency over manually-designed va…
Mitigating Memory Wall Effects in CNN Engines with On-the-Fly Weights Generation
Stylianos I. Venieris, Javier Fernandez-Marques, Nicholas D. Lane
The unprecedented accuracy of convolutional neural networks (CNNs) across a broad range of AI tasks has led to their widespread deployment in mobile and embedded settings. In a pur…
Pollen: High-throughput Federated Learning Simulation via Resource-Aware Client Placement
Lorenzo Sani, Pedro Porto Buarque de Gusmão, Alex Iacob +5
Federated Learning (FL) is a privacy-focused machine learning paradigm that collaboratively trains models directly on edge devices. Simulation plays an essential role in FL adoptio…
PQA: Exploring the Potential of Product Quantization in DNN Hardware Acceleration
Ahmed F. AbouElhamayed, Angela Cui, Javier Fernandez-Marques +2
Conventional multiply-accumulate (MAC) operations have long dominated computation time for deep neural networks (DNNs), espcially convolutional neural networks (CNNs). Recently, pr…
Federated Learning for Inference at Anytime and Anywhere
Zicheng Liu, Da Li, Javier Fernandez-Marques +6
Federated learning has been predominantly concerned with collaborative training of deep networks from scratch, and especially the many challenges that arise, such as communication…
Match to Win: Analysing Sequences Lengths for Efficient Self-supervised Learning in Speech and Audio
Yan Gao, Javier Fernandez-Marques, Titouan Parcollet +2
Self-supervised learning (SSL) has proven vital in speech and audio-related applications. The paradigm trains a general model on unlabeled data that can later be used to solve spec…
Protea: Client Profiling within Federated Systems using Flower
Wanru Zhao, Xinchi Qiu, Javier Fernandez-Marques +2
Federated Learning (FL) has emerged as a prospective solution that facilitates the training of a high-performing centralised model without compromising the privacy of users. While…
ZeroFL: Efficient On-Device Training for Federated Learning with Local Sparsity
Xinchi Qiu, Javier Fernandez-Marques, Pedro PB Gusmao +3
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 qualit…
FedorAS: Federated Architecture Search under system heterogeneity
Lukasz Dudziak, Stefanos Laskaridis, Javier Fernandez-Marques
Federated learning (FL) has recently gained considerable attention due to its ability to learn on decentralised data while preserving client privacy. However, it also poses additio…
Federated Self-supervised Speech Representations: Are We There Yet?
Yan Gao, Javier Fernandez-Marques, Titouan Parcollet +2
The ubiquity of microphone-enabled devices has lead to large amounts of unlabelled audio data being produced at the edge. The integration of self-supervised learning (SSL) and fede…
On-device Federated Learning with Flower
Akhil Mathur, Daniel J. Beutel, Pedro Porto Buarque de Gusmão +6
Federated Learning (FL) allows edge devices to collaboratively learn a shared prediction model while keeping their training data on the device, thereby decoupling the ability to do…
unzipFPGA: Enhancing FPGA-based CNN Engines with On-the-Fly Weights Generation
Stylianos I. Venieris, Javier Fernandez-Marques, Nicholas D. Lane
Single computation engines have become a popular design choice for FPGA-based convolutional neural networks (CNNs) enabling the deployment of diverse models without fabric reconfig…
End-to-End Speech Recognition from Federated Acoustic Models
Yan Gao, Titouan Parcollet, Salah Zaiem +4
Training Automatic Speech Recognition (ASR) models under federated learning (FL) settings has attracted a lot of attention recently. However, the FL scenarios often presented in th…
Degree-Quant: Quantization-Aware Training for Graph Neural Networks
Shyam A. Tailor, Javier Fernandez-Marques, Nicholas D. Lane
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…
Searching for Winograd-aware Quantized Networks
Javier Fernandez-Marques, Paul N. Whatmough, Andrew Mundy +1
Lightweight architectural designs of Convolutional Neural Networks (CNNs) together with quantization have paved the way for the deployment of demanding computer vision applications…