1,121 research outputs found
A survey on vulnerability of federated learning: A learning algorithm perspective
Federated Learning (FL) has emerged as a powerful paradigm for training Machine Learning (ML), particularly Deep Learning (DL) models on multiple devices or servers while maintaining data localized at owners’ sites. Without centralizing data, FL holds promise for scenarios where data integrity, privacy and security and are critical. However, this decentralized training process also opens up new avenues for opponents to launch unique attacks, where it has been becoming an urgent need to understand the vulnerabilities and corresponding defense mechanisms from a learning algorithm perspective. This review paper takes a comprehensive look at malicious attacks against FL, categorizing them from new perspectives on attack origins and targets, and providing insights into their methodology and impact. In this survey, we focus on threat models targeting the learning process of FL systems. Based on the source and target of the attack, we categorize existing threat models into four types, Data to Model (D2M), Model to Data (M2D), Model to Model (M2M) and composite attacks. For each attack type, we discuss the defense strategies proposed, highlighting their effectiveness, assumptions and potential areas for improvement. Defense strategies have evolved from using a singular metric to excluding malicious clients, to employing a multifaceted approach examining client models at various phases. In this survey paper, our research indicates that the to-learn data, the learning gradients, and the learned model at different stages all can be manipulated to initiate malicious attacks that range from undermining model performance, reconstructing private local data, and to inserting backdoors. We have also seen these threat are becoming more insidious. While earlier studies typically amplified malicious gradients, recent endeavors subtly alter the least significant weights in local models to bypass defense measures. This literature review provides a holistic understanding of the current FL threat landscape and highlights the importance of developing robust, efficient, and privacy-preserving defenses to ensure the safe and trusted adoption of FL in real-world applications. The categorized bibliography can be found at: https://github.com/Rand2AI/Awesome-Vulnerability-of-Federated-Learning
A survey on vulnerability of federated learning: A learning algorithm perspective
Federated Learning (FL) has emerged as a powerful paradigm for training Machine Learning (ML), particularly Deep Learning (DL) models on multiple devices or servers while maintaining data localized at owners’ sites. Without centralizing data, FL holds promise for scenarios where data integrity, privacy and security and are critical. However, this decentralized training process also opens up new avenues for opponents to launch unique attacks, where it has been becoming an urgent need to understand the vulnerabilities and corresponding defense mechanisms from a learning algorithm perspective. This review paper takes a comprehensive look at malicious attacks against FL, categorizing them from new perspectives on attack origins and targets, and providing insights into their methodology and impact. In this survey, we focus on threat models targeting the learning process of FL systems. Based on the source and target of the attack, we categorize existing threat models into four types, Data to Model (D2M), Model to Data (M2D), Model to Model (M2M) and composite attacks. For each attack type, we discuss the defense strategies proposed, highlighting their effectiveness, assumptions and potential areas for improvement. Defense strategies have evolved from using a singular metric to excluding malicious clients, to employing a multifaceted approach examining client models at various phases. In this survey paper, our research indicates that the to-learn data, the learning gradients, and the learned model at different stages all can be manipulated to initiate malicious attacks that range from undermining model performance, reconstructing private local data, and to inserting backdoors. We have also seen these threat are becoming more insidious. While earlier studies typically amplified malicious gradients, recent endeavors subtly alter the least significant weights in local models to bypass defense measures. This literature review provides a holistic understanding of the current FL threat landscape and highlights the importance of developing robust, efficient, and privacy-preserving defenses to ensure the safe and trusted adoption of FL in real-world applications. The categorized bibliography can be found at: https://github.com/Rand2AI/Awesome-Vulnerability-of-Federated-Learning
A BIM - GIS Integrated Information Model Using Semantic Web and RDF Graph Databases
In recent years, 3D virtual indoor and outdoor urban modelling has become an essential geospatial information framework for civil and engineering applications such as emergency response, evacuation planning, and facility management. Building multi-sourced and multi-scale 3D urban models are in high demand among architects, engineers, and construction professionals to achieve these tasks and provide relevant information to decision support systems. Spatial modelling technologies such as Building Information Modelling (BIM) and Geographical Information Systems (GIS) are frequently used to meet such high demands. However, sharing data and information between these two domains is still challenging. At the same time, the semantic or syntactic strategies for inter-communication between BIM and GIS do not fully provide rich semantic and geometric information exchange of BIM into GIS or vice-versa. This research study proposes a novel approach for integrating BIM and GIS using semantic web technologies and Resources Description Framework (RDF) graph databases. The suggested solution's originality and novelty come from combining the advantages of integrating BIM and GIS models into a semantically unified data model using a semantic framework and ontology engineering approaches. The new model will be named Integrated Geospatial Information Model (IGIM). It is constructed through three stages. The first stage requires BIMRDF and GISRDF graphs generation from BIM and GIS datasets. Then graph integration from BIM and GIS semantic models creates IGIMRDF. Lastly, the information from IGIMRDF unified graph is filtered using a graph query language and graph data analytics tools. The linkage between BIMRDF and GISRDF is completed through SPARQL endpoints defined by queries using elements and entity classes with similar or complementary information from properties, relationships, and geometries from an ontology-matching process during model construction. The resulting model (or sub-model) can be managed in a graph database system and used in the backend as a data-tier serving web services feeding a front-tier domain-oriented application. A case study was designed, developed, and tested using the semantic integrated information model for validating the newly proposed solution, architecture, and performance
A multi-user process interface system for a process control computer
This thesis describes a system to implement a distributed multi-user process interface to allow the PDP-11/23 computer in the Electrical Engineering department at UCT to be used for process control. The use of this system is to be shared between postgraduate students for research and undergraduates for doing real-time control projects. The interface may be used concurrently by several users, and access is controlled in such a way as to prevent users' programs from interfering with one another. The process interface hardware used was a GEC Micro-Media system, which is a stand-alone process interface system communicating with a host (the PDP-11/23) via a serial line. Hardware to drive a 600-metre serial link at 9600 baud between the PDP-11/23 and the Media interface was designed and built. The software system on the host, written in RTL/2, holds-all data from the interface in a resident common data-base and continually updates it. Access to the interface by applications programs is done indirectly by reading and writing to the database, for which purpose a library of user interface routines is provided. To allow future expansion and modification of the Media interface, software (also written in RTL/2) for an LSI-11 minicomputer interfaced to the Media bus was developed which emulates the operation of the GEC proprietary Micro-Media software. A program to download this software into the LSI-11 was written. A suite of diagnostic programs enables testing of the system hardware and software at various levels. To ease testing, teaching, and applications programming, a general-purpose simulation package for the simulation of analogue systems was developed, as well as graphics routines for use with a Tektronix 4010 plotting terminal. A. real-time computing project for a class of undergraduates was run in 1983. This project made extensive use of the system and demonstrated its viability
Acceptability of speed limits and other policy measures in German cities
An increasing number of German cities currently demand the Federal Government to empower cities to implement 30 kph speed limits at their own discretion. Setting area-wide 30 kph as the maximum speed, as already installed in many other European cities, could therefore soon become a viable policy option in Germany.
This thesis conducts a stated choice (SC) experiment to determine the acceptability of such area-wide standard 30 kph speed limits compared to the acceptability of the expansion of shared space zones, costs for inner-city on-street car parking and public transport ticket fares. Combining the policies as attributes in an unlabeled experiment allows to juxtapose the policies in terms of their relative importance for the respondents’ choice decision. 129 adults from German cities with more than 100,000 inhabitants participated in an online survey during September 2022.
The results show that respondents evaluate the introduction of standard 30 kph speed limit in the city center as utility increasing compared to the prevalent status quo with standard 50 kph. Setting a standard 30 kph speed limit in the whole city also has a positive parameter in the base model, but does not significantly influence the respondents’ utility. The expansion of shared space seems to have no relevant effect on the choice decision of respondents. Higher ticket fares for public transport show to be utility decreasing for respondents of this study, whereas an increase in car parking costs is assessed positively.
Clear differences in the policy assessment of different subgroups of respondents can be observed. In line with literature, city-wide implementation of a standard 30 kph speed limit shows low acceptability among the group of frequent car users. In turn, voters of mayoral candidates for the Green Party (Bündnis 90/Die Grünen) or Left Party (Die Linke) expect a positive effect on their personal utility when a standard 30 kph speed limit is established in the whole city or in the city center only. Respondents’ gender does not seem to affect the assessment of 30 kph speed limit policy
Automatic Generation of Personalized Recommendations in eCoaching
Denne avhandlingen omhandler eCoaching for personlig livsstilsstøtte i sanntid ved bruk av informasjons- og kommunikasjonsteknologi. Utfordringen er å designe, utvikle og teknisk evaluere en prototyp av en intelligent eCoach som automatisk genererer personlige og evidensbaserte anbefalinger til en bedre livsstil. Den utviklede løsningen er fokusert på forbedring av fysisk aktivitet. Prototypen bruker bærbare medisinske aktivitetssensorer. De innsamlede data blir semantisk representert og kunstig intelligente algoritmer genererer automatisk meningsfulle, personlige og kontekstbaserte anbefalinger for mindre stillesittende tid. Oppgaven bruker den veletablerte designvitenskapelige forskningsmetodikken for å utvikle teoretiske grunnlag og praktiske implementeringer. Samlet sett fokuserer denne forskningen på teknologisk verifisering snarere enn klinisk evaluering.publishedVersio
Joint Path planning and Power Allocation of a Cellular-Connected UAV using Apprenticeship Learning via Deep Inverse Reinforcement Learning
This paper investigates an interference-aware joint path planning and power
allocation mechanism for a cellular-connected unmanned aerial vehicle (UAV) in
a sparse suburban environment. The UAV's goal is to fly from an initial point
and reach a destination point by moving along the cells to guarantee the
required quality of service (QoS). In particular, the UAV aims to maximize its
uplink throughput and minimize the level of interference to the ground user
equipment (UEs) connected to the neighbor cellular BSs, considering the
shortest path and flight resource limitation. Expert knowledge is used to
experience the scenario and define the desired behavior for the sake of the
agent (i.e., UAV) training. To solve the problem, an apprenticeship learning
method is utilized via inverse reinforcement learning (IRL) based on both
Q-learning and deep reinforcement learning (DRL). The performance of this
method is compared to learning from a demonstration technique called behavioral
cloning (BC) using a supervised learning approach. Simulation and numerical
results show that the proposed approach can achieve expert-level performance.
We also demonstrate that, unlike the BC technique, the performance of our
proposed approach does not degrade in unseen situations
Excluding Surfaces as Minors in Graphs
We introduce an annotated extension of treewidth that measures the
contribution of a vertex set to the treewidth of a graph This notion
provides a graph distance measure to some graph property : A
vertex set is a -treewidth modulator of to if the
treewidth of in is at most and its removal gives a graph in
This notion allows for a version of the Graph Minors Structure
Theorem (GMST) that has no need for apices and vortices: -minor free
graphs are those that admit tree-decompositions whose torsos have
-treewidth modulators to some surface of Euler-genus This
reveals that minor-exclusion is essentially tree-decomposability to a
``modulator-target scheme'' where the modulator is measured by its treewidth
and the target is surface embeddability. We then fix the target condition by
demanding that is some particular surface and define a ``surface
extension'' of treewidth, where \Sigma\mbox{-}\mathsf{tw}(G) is the minimum
for which admits a tree-decomposition whose torsos have a -treewidth
modulator to being embeddable in We identify a finite collection
of parametric graphs and prove that the minor-exclusion
of the graphs in precisely determines the asymptotic
behavior of {\Sigma}\mbox{-}\mathsf{tw}, for every surface It
follows that the collection bijectively corresponds to
the ``surface obstructions'' for i.e., surfaces that are minimally
non-contained in $\Sigma.
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