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Found 21 papers, 5 papers with code
Evaluating Privacy Leakage in Split Learning
In particular, on-device machine learning allows us to avoid sharing raw data with a third-party server during inference.
GPU-based Private Information Retrieval for On-Device Machine Learning Inference
Together, for various on-device ML applications such as recommendation and language modeling, our system on a single V100 GPU can serve up to $100, 000$ queries per second -- a $>100 \times$ throughput improvement over a CPU-based baseline -- while maintaining model accuracy.
FEL: High Capacity Learning for Recommendation and Ranking via Federated Ensemble Learning
Despite FL's initial success, many important deep learning use cases, such as ranking and recommendation tasks, have been limited from on-device learning.
Smart at what cost? Characterising Mobile Deep Neural Networks in the wild
To this end, we analyse over 16k of the most popular apps in the Google Play Store to characterise their DNN usage and performance across devices of different capabilities, both across tiers and generations.
How to Reach Real-Time AI on Consumer Devices? Solutions for Programmable and Custom Architectures
Collectively, these results highlight the critical need for further exploration as to how the various cross-stack solutions can be best combined in order to bring the latest advances in deep learning close to users, in a robust and efficient manner.
DynO: Dynamic Onloading of Deep Neural Networks from Cloud to Device
Recently, there has been an explosive growth of mobile and embedded applications using convolutional neural networks(CNNs).
FjORD: Fair and Accurate Federated Learning under heterogeneous targets with Ordered Dropout
FjORD alleviates the problem of client system heterogeneity by tailoring the model width to the client's capabilities.
It's always personal: Using Early Exits for Efficient On-Device CNN Personalisation
On-device machine learning is becoming a reality thanks to the availability of powerful hardware and model compression techniques.
SPINN: Synergistic Progressive Inference of Neural Networks over Device and Cloud
Despite the soaring use of convolutional neural networks (CNNs) in mobile applications, uniformly sustaining high-performance inference on mobile has been elusive due to the excessive computational demands of modern CNNs and the increasing diversity of deployed devices.
DarkneTZ: Towards Model Privacy at the Edge using Trusted Execution Environments
We present DarkneTZ, a framework that uses an edge device's Trusted Execution Environment (TEE) in conjunction with model partitioning to limit the attack surface against Deep Neural Networks (DNNs).
Detecting Cyberbullying and Cyberaggression in Social Media
We also explore specific manifestations of abusive behavior, i. e., cyberbullying and cyberaggression, in one of the hate-related communities (Gamergate).
EmBench: Quantifying Performance Variations of Deep Neural Networks across Modern Commodity Devices
In recent years, advances in deep learning have resulted in unprecedented leaps in diverse tasks spanning from speech and object recognition to context awareness and health monitoring.
A Self-Attentive Emotion Recognition Network
Modern deep learning approaches have achieved groundbreaking performance in modeling and classifying sequential data.
Finding Dory in the Crowd: Detecting Social Interactions using Multi-Modal Mobile Sensing
Remembering our day-to-day social interactions is challenging even if you aren't a blue memory challenged fish.
Large Scale Crowdsourcing and Characterization of Twitter Abusive Behavior
In recent years, offensive, abusive and hateful language, sexism, racism and other types of aggressive and cyberbullying behavior have been manifesting with increased frequency, and in many online social media platforms.
Social and Information Networks 68T06 K.4.2
A Unified Deep Learning Architecture for Abuse Detection
Hate speech, offensive language, sexism, racism and other types of abusive behavior have become a common phenomenon in many online social media platforms.
Continual Prediction of Notification Attendance with Classical and Deep Network Approaches
Besides using classical gradient-boosted trees, we demonstrate how to make continual predictions using a recurrent neural network (RNN).
Human-Computer Interaction
Class-based Prediction Errors to Detect Hate Speech with Out-of-vocabulary Words
Common approaches to text categorization essentially rely either on n-gram counts or on word embeddings.
Practical Processing of Mobile Sensor Data for Continual Deep Learning Predictions
We present a practical approach for processing mobile sensor time series data for continual deep learning predictions.
Hot or not? Forecasting cellular network hot spots using sector performance indicators
Our results indicate that, compared to the best baseline, tree-based models can deliver up to 14% better forecasts for regular hot spots and 153% better forecasts for non-regular hot spots.
The architecture of innovation: Tracking face-to-face interactions with ubicomp technologies
The layouts of the buildings we live in shape our everyday lives.
Computers and Society Human-Computer Interaction Social and Information Networks
