Search Results for author:
Found 13 papers, 3 papers with code
Federated Learning for Inference at Anytime and Anywhere
Federated learning has been predominantly concerned with collaborative training of deep networks from scratch, and especially the many challenges that arise, such as communication cost, robustness to heterogeneous data, and support for diverse device capabilities.
Match to Win: Analysing Sequences Lengths for Efficient Self-supervised Learning in Speech and Audio
Self-supervised learning (SSL) has proven vital in speech and audio-related applications.
ZeroFL: Efficient On-Device Training for Federated Learning with Local Sparsity
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.
Protea: Client Profiling within Federated Systems using Flower
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.
FedorAS: Federated Architecture Search under system heterogeneity
In this paper we explore the question of whether we can design architectures of different footprints in a cross-device federated setting, where the device landscape, availability and scale are very different.
Federated Self-supervised Speech Representations: Are We There Yet?
The ubiquity of microphone-enabled devices has lead to large amounts of unlabelled audio data being produced at the edge.
End-to-End Speech Recognition from Federated Acoustic Models
Training Automatic Speech Recognition (ASR) models under federated learning (FL) settings has attracted a lot of attention recently.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
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On-device Federated Learning with Flower
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 machine learning from the need to store data in the cloud.
unzipFPGA: Enhancing FPGA-based CNN Engines with On-the-Fly Weights Generation
Single computation engines have become a popular design choice for FPGA-based convolutional neural networks (CNNs) enabling the deployment of diverse models without fabric reconfiguration.
A first look into the carbon footprint of federated learning
Despite impressive results, deep learning-based technologies also raise severe privacy and environmental concerns induced by the training procedure often conducted in data centers.
Degree-Quant: Quantization-Aware Training for Graph Neural Networks
Graph neural networks (GNNs) have demonstrated strong performance on a wide variety of tasks due to their ability to model non-uniform structured data.
Flower: A Friendly Federated Learning Research Framework
Federated Learning (FL) has emerged as a promising technique for edge devices to collaboratively learn a shared prediction model, while keeping their training data on the device, thereby decoupling the ability to do machine learning from the need to store the data in the cloud.
Searching for Winograd-aware Quantized Networks
Lightweight architectural designs of Convolutional Neural Networks (CNNs) together with quantization have paved the way for the deployment of demanding computer vision applications on mobile devices.
