A locally-built, LLM-digested index of recent arXiv papers in quant finance, geometry/topology, and statistical ML — keyword search served straight from SQLite on this machine.
Survey of IoT recommendation systems and their limitations.
State-of-the-art image recognition systems use sophisticated Convolutional Neural Networks (CNNs) that are designed and trained to identify numerous object classes. Such networks are fairly resource intensive to compute, prohibiting their deployment on resource-constrained embedded platforms. On one hand, the ability t…
Severe constraints on memory and computation characterizing the Internet-of-Things (IoT) units may prevent the execution of Deep Learning (DL)-based solutions, which typically demand large memory and high processing load. In order to support a real-time execution of the considered DL model at the IoT unit level, DL sol…
The Internet of Things (IoT) extends the Internet connectivity into billions of IoT devices around the world, where the IoT devices collect and share information to reflect status of the physical world. The Autonomous Control System (ACS), on the other hand, performs control functions on the physical systems without ex…
IoT nodes compress measurements into DNN outputs for efficient communication.
Machine learning-based applications are increasingly prevalent in IoT devices. The power and storage constraints of these devices make it particularly challenging to run modern neural networks, limiting the number of new applications that can be deployed on an IoT system. A number of compression techniques have been pr…
Deep learning identifies unknown IoT devices in network traffic.
Many Internet-of-Things (IoT) applications demand fast and accurate understanding of a few key events in their surrounding environment. Deep Convolutional Neural Networks (CNNs) have emerged as an effective approach to understand speech, images, and similar high dimensional data types. Algorithmic performance of modern…
To reap the benefits of the Internet of Things (IoT), it is imperative to secure the system against cyber attacks in order to enable mission critical and real-time applications. To this end, intrusion detection systems (IDSs) have been widely used to detect anomalies caused by a cyber attacker in IoT systems. However, …
Implementing large-scale deep neural networks with high computational complexity on low-cost IoT devices may inevitably be constrained by limited computation resource, making the devices hard to respond in real-time. This disjunction makes the state-of-art deep learning algorithms, i.e. CNN (Convolutional Neural Networ…
The future Internet of Things (IoT) will have a deep economical, commercial and social impact on our lives. The participating nodes in IoT networks are usually resource-constrained, which makes them luring targets for cyber attacks. In this regard, extensive efforts have been made to address the security and privacy is…
Survey examines ML for IoT security, addressing new challenges.
This review explores federated learning for IoT data privacy.
This paper reviews methods to create compact neural networks for IoT applications.
The paper addresses challenges in edge deep learning for IoT, proposing new directions.
The Internet of Things (IoT) will be a main data generation infrastructure for achieving better system intelligence. However, the extensive data collection and processing in IoT also engender various privacy concerns. This paper provides a taxonomy of the existing privacy-preserving machine learning approaches develope…
Recently, a wide range of smart devices are deployed in a variety of environments to improve the quality of human life. One of the important IoT-based applications is smart homes for healthcare, especially for elders. IoT-based smart homes enable elders' health to be properly monitored and taken care of. However, elder…
Paper uses ensemble learning for IoT cybersecurity anomaly detection.
Recent advances in deep learning motivate the use of deep neural networks in Internet-of-Things (IoT) applications. These networks are modelled after signal processing in the human brain, thereby leading to significant advantages at perceptual tasks such as vision and speech recognition. IoT applications, however, ofte…
This paper compares machine and deep learning algorithms for IoT data classification.
The paper uses interpretable ML to secure data quality in IoT edge computing.
Paper removes sensitive data from IoT and Big Data for privacy.
FedHDPrivacy uses DP to improve FL in IoT, maintaining high accuracy.
The significant computational requirements of deep learning present a major bottleneck for its large-scale adoption on hardware-constrained IoT-devices. Here, we envision a new paradigm called EdgeAI to address major impediments associated with deploying deep networks at the edge. Specifically, we discuss the existing …
Adaptive anomaly detection for IoT data reduces delay without sacrificing accuracy.
A new deep learning framework for efficient IoT data compression and inference.
We propose a real-time context-aware learning system along with the architecture that runs on the mobile devices, provide services to the user and manage the IoT devices. In this system, an application running on mobile devices collected data from the sensors, learned about the user-defined context, made predictions in…
Advances in deep neural networks (DNN) greatly bolster real-time detection of anomalous IoT data. However, IoT devices can barely afford complex DNN models due to limited computational power and energy supply. While one can offload anomaly detection tasks to the cloud, it incurs long delay and requires large bandwidth …
Study detects and mitigates stealthy DDoS attacks in IoT networks.
Federated Learning helps IoT devices learn without central servers.
A new machine learning framework reduces IoT data transfer by two orders of magnitude.
New method reduces cloud usage for mobile/IoT predictions.
DOMKL learns functions from IoT data with minimal regret and consensus constraints.
Study categorizes time series anomaly detection metrics based on evaluation challenges.
PairNets optimize AI models for fast IoT applications.
By leveraging the concept of mobile edge computing (MEC), massive amount of data generated by a large number of Internet of Things (IoT) devices could be offloaded to MEC server at the edge of wireless network for further computational intensive processing. However, due to the resource constraint of IoT devices and wir…
The recent success of single-agent reinforcement learning (RL) in Internet of things (IoT) systems motivates the study of multi-agent reinforcement learning (MARL), which is more challenging but more useful in large-scale IoT. In this paper, we consider a voting-based MARL problem, in which the agents vote to make grou…
New algorithms improve signal processing in federated learning.
Paper proposes DP-PASGD for efficient, private IoT learning.
Meta-ensemble scheme allocates queries to EC nodes for reduced latency.
Researchers analyze inverse optimal transport, deriving theoretical and empirical insights.
FDA3 defends IIoT applications against adversarial attacks by federating defense knowledge.
Predictive Q-learning algorithm for IoT networks with human operators.
Detecting patterns in real time streaming data has been an interesting and challenging data analytics problem. With the proliferation of a variety of sensor devices, real-time analytics of data from the Internet of Things (IoT) to learn regular and irregular patterns has become an important machine learning problem to …
Optimal sampling reduces power grid data analysis costs.
The paper examines the feasibility of managing aggregate cyber-risk in IoT environments.
Energy-efficient DL inference for IoT devices reduces power consumption and improves performance.
Paper proposes DRL for unsupervised IoT localization.