8 research outputs found
Collaborative Inference in DNN-based Satellite Systems with Dynamic Task Streams
As a driving force in the advancement of intelligent in-orbit applications,
DNN models have been gradually integrated into satellites, producing daily
latency-constraint and computation-intensive tasks. However, the substantial
computation capability of DNN models, coupled with the instability of the
satellite-ground link, pose significant challenges, hindering timely completion
of tasks. It becomes necessary to adapt to task stream changes when dealing
with tasks requiring latency guarantees, such as dynamic observation tasks on
the satellites. To this end, we consider a system model for a collaborative
inference system with latency constraints, leveraging the multi-exit and model
partition technology. To address this, we propose an algorithm, which is
tailored to effectively address the trade-off between task completion and
maintaining satisfactory task accuracy by dynamically choosing early-exit and
partition points. Simulation evaluations show that our proposed algorithm
significantly outperforms baseline algorithms across the task stream with
strict latency constraints
Federated Domain Generalization: A Survey
Machine learning typically relies on the assumption that training and testing
distributions are identical and that data is centrally stored for training and
testing. However, in real-world scenarios, distributions may differ
significantly and data is often distributed across different devices,
organizations, or edge nodes. Consequently, it is imperative to develop models
that can effectively generalize to unseen distributions where data is
distributed across different domains. In response to this challenge, there has
been a surge of interest in federated domain generalization (FDG) in recent
years. FDG combines the strengths of federated learning (FL) and domain
generalization (DG) techniques to enable multiple source domains to
collaboratively learn a model capable of directly generalizing to unseen
domains while preserving data privacy. However, generalizing the federated
model under domain shifts is a technically challenging problem that has
received scant attention in the research area so far. This paper presents the
first survey of recent advances in this area. Initially, we discuss the
development process from traditional machine learning to domain adaptation and
domain generalization, leading to FDG as well as provide the corresponding
formal definition. Then, we categorize recent methodologies into four classes:
federated domain alignment, data manipulation, learning strategies, and
aggregation optimization, and present suitable algorithms in detail for each
category. Next, we introduce commonly used datasets, applications, evaluations,
and benchmarks. Finally, we conclude this survey by providing some potential
research topics for the future
Sabrina: Modeling and Visualization of Economy Data with Incremental Domain Knowledge
Investment planning requires knowledge of the financial landscape on a large
scale, both in terms of geo-spatial and industry sector distribution. There is
plenty of data available, but it is scattered across heterogeneous sources
(newspapers, open data, etc.), which makes it difficult for financial analysts
to understand the big picture. In this paper, we present Sabrina, a financial
data analysis and visualization approach that incorporates a pipeline for the
generation of firm-to-firm financial transaction networks. The pipeline is
capable of fusing the ground truth on individual firms in a region with
(incremental) domain knowledge on general macroscopic aspects of the economy.
Sabrina unites these heterogeneous data sources within a uniform visual
interface that enables the visual analysis process. In a user study with three
domain experts, we illustrate the usefulness of Sabrina, which eases their
analysis process
Utilizing AI planning on the edge
The convergence between AI planning techniques and the Internet of Things (IoT) can solve various operational and business challenges. However, IoT systems’ stringent requirements such as latency and scalability have introduced several challenges to execute and scale planners in cloud environments. Edge computers placed close to the IoT domain (e.g., sensors) can be leveraged for implementing planners and overcoming scalability issues. We propose a conceptual framework highlighting executing Expressive Numeric Heuristic Search Planner on distributed devices in edge networks. As a proof of concept, we develop a simulator to show the applicability and feasibility of running planners on the edge. As a case study, we simulate a waste management problem and find the optimal route for disposing of? waste bins in a city. Throughout the experiments, the user can discover insightful information regarding the planner’s applicability on the edge.This work was supported in part by the “Smart Communities and Technologies (Smart CT)” and it has received funding from the EU’s Horizon 2020 Research and Innovation Programme under grant agreement No. 871525
A decentralized approach for determining configurator placement in dynamic edge networks
2020 IEEE Second International Conference on Cognitive Machine Intelligence (CogMI
Stochastic Modeling for Intelligent Software-Defined Vehicular Networks: A Survey
Digital twins and the Internet of Things (IoT) have gained significant research attention in recent years due to their potential advantages in various domains, and vehicular ad hoc networks (VANETs) are one such application. VANETs can provide a wide range of services for passengers and drivers, including safety, convenience, and information. The dynamic nature of these environments poses several challenges, including intermittent connectivity, quality of service (QoS), and heterogeneous applications. Combining intelligent technologies and software-defined networking (SDN) with VANETs (termed intelligent software-defined vehicular networks (iSDVNs)) meets these challenges. In this context, several types of research have been published, and we summarize their benefits and limitations. We also aim to survey stochastic modeling and performance analysis for iSDVNs and the uses of machine-learning algorithms through digital twin networks (DTNs), which are also part of iSDVNs. We first present a taxonomy of SDVN architectures based on their modes of operation. Next, we survey and classify the state-of-the-art iSDVN routing protocols, stochastic computations, and resource allocations. The evolution of SDN causes its complexity to increase, posing a significant challenge to efficient network management. Digital twins offer a promising solution to address these challenges. This paper explores the relationship between digital twins and SDN and also proposes a novel approach to improve network management in SDN environments by increasing digital twin capabilities. We analyze the pitfalls of these state-of-the-art iSDVN protocols and compare them using tables. Finally, we summarize several challenges faced by current iSDVNs and possible future directions to make iSDVNs autonomous