3,588 research outputs found
Decentralized Federated Learning: Fundamentals, State-of-the-art, Frameworks, Trends, and Challenges
In the last decade, Federated Learning (FL) has gained relevance in training
collaborative models without sharing sensitive data. Since its birth,
Centralized FL (CFL) has been the most common approach in the literature, where
a central entity creates a global model. However, a centralized approach leads
to increased latency due to bottlenecks, heightened vulnerability to system
failures, and trustworthiness concerns affecting the entity responsible for the
global model creation. Decentralized Federated Learning (DFL) emerged to
address these concerns by promoting decentralized model aggregation and
minimizing reliance on centralized architectures. However, despite the work
done in DFL, the literature has not (i) studied the main aspects
differentiating DFL and CFL; (ii) analyzed DFL frameworks to create and
evaluate new solutions; and (iii) reviewed application scenarios using DFL.
Thus, this article identifies and analyzes the main fundamentals of DFL in
terms of federation architectures, topologies, communication mechanisms,
security approaches, and key performance indicators. Additionally, the paper at
hand explores existing mechanisms to optimize critical DFL fundamentals. Then,
the most relevant features of the current DFL frameworks are reviewed and
compared. After that, it analyzes the most used DFL application scenarios,
identifying solutions based on the fundamentals and frameworks previously
defined. Finally, the evolution of existing DFL solutions is studied to provide
a list of trends, lessons learned, and open challenges
Semantic-Based, Scalable, Decentralized and Dynamic Resource Discovery for Internet-Based Distributed System
Resource Discovery (RD) is a key issue in Internet-based distributed sytems such as
grid. RD is about locating an appropriate resource/service type that matches the user's
application requirements. This is very important, as resource reservation and task
scheduling are based on it. Unfortunately, RD in grid is very challenging as resources
and users are distributed, resources are heterogeneous in their platforms, status of the
resources is dynamic (resources can join or leave the system without any prior notice)
and most recently the introduction of a new type of grid called intergrid (grid of grids)
with the use of multi middlewares. Such situation requires an RD system that has rich
interoperability, scalability, decentralization and dynamism features. However,
existing grid RD systems have difficulties to attain these features. Not only that, they
lack the review and evaluation studies, which may highlight the gap in achieving the
required features. Therefore, this work discusses the problem associated with intergrid
RD from two perspectives. First, reviewing and classifying the current grid RD
systems in such a way that may be useful for discussing and comparing them. Second,
propose a novel RD framework that has the aforementioned required RD features. In
the former, we mainly focus on the studies that aim to achieve interoperability in the
first place, which are known as RD systems that use semantic information (semantic
technology). In particular, we classify such systems based on their qualitative use of
the semantic information. We evaluate the classified studies based on their degree of
accomplishment of interoperability and the other RD requirements, and draw the
future research direction of this field. Meanwhile in the latter, we name the new
framework as semantic-based scalable decentralized dynamic RD. The framework
further contains two main components which are service description, and service
registration and discovery models. The earlier consists of a set of ontologies and
services. Ontologies are used as a data model for service description, whereas the
services are to accomplish the description process. The service registration is also based on ontology, where nodes of the service (service providers) are classified to
some classes according to the ontology concepts, which means each class represents a
concept in the ontology. Each class has a head, which is elected among its own class
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nodes/members. Head plays the role of a registry in its class and communicates with
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the other heads of the classes in a peer to peer manner during the discovery process.
We further introduce two intelligent agents to automate the discovery process which
are Request Agent (RA) and Description Agent (DA). Eaclj. node is supposed to have
both agents. DA describes the service capabilities based on the ontology, and RA
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carries the service requests based on the ontology as well. We design a service search
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algorithm for the RA that starts the service look up from the class of request origin
first, then to the other classes.
We finally evaluate the performance of our framework ~ith extensive simulation
experiments, the result of which confirms the effectiveness of the proposed system in
satisfying the required RD features (interoperability, scalability, decentralization and
dynamism). In short, our main contributions are outlined new key taxonomy for the
semantic-based grid RD studies; an interoperable semantic description RD component
model for intergrid services metadata representation; a semantic distributed registry
architecture for indexing service metadata; and an agent-qased service search and
selection algorithm.
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Investigating biocomplexity through the agent-based paradigm.
Capturing the dynamism that pervades biological systems requires a computational approach that can accommodate both the continuous features of the system environment as well as the flexible and heterogeneous nature of component interactions. This presents a serious challenge for the more traditional mathematical approaches that assume component homogeneity to relate system observables using mathematical equations. While the homogeneity condition does not lead to loss of accuracy while simulating various continua, it fails to offer detailed solutions when applied to systems with dynamically interacting heterogeneous components. As the functionality and architecture of most biological systems is a product of multi-faceted individual interactions at the sub-system level, continuum models rarely offer much beyond qualitative similarity. Agent-based modelling is a class of algorithmic computational approaches that rely on interactions between Turing-complete finite-state machines--or agents--to simulate, from the bottom-up, macroscopic properties of a system. In recognizing the heterogeneity condition, they offer suitable ontologies to the system components being modelled, thereby succeeding where their continuum counterparts tend to struggle. Furthermore, being inherently hierarchical, they are quite amenable to coupling with other computational paradigms. The integration of any agent-based framework with continuum models is arguably the most elegant and precise way of representing biological systems. Although in its nascence, agent-based modelling has been utilized to model biological complexity across a broad range of biological scales (from cells to societies). In this article, we explore the reasons that make agent-based modelling the most precise approach to model biological systems that tend to be non-linear and complex
Geospatial Tessellation in the Agent-In-Cell Model: A Framework for Agent-Based Modeling of Pandemic
Agent-based simulation is a versatile and potent computational modeling
technique employed to analyze intricate systems and phenomena spanning diverse
fields. However, due to their computational intensity, agent-based models
become more resource-demanding when geographic considerations are introduced.
This study delves into diverse strategies for crafting a series of Agent-Based
Models, named "agent-in-the-cell," which emulate a city. These models,
incorporating geographical attributes of the city and employing real-world
open-source mobility data from Safegraph's publicly available dataset, simulate
the dynamics of COVID spread under varying scenarios. The "agent-in-the-cell"
concept designates that our representative agents, called meta-agents, are
linked to specific home cells in the city's tessellation. We scrutinize
tessellations of the mobility map with varying complexities and experiment with
the agent density, ranging from matching the actual population to reducing the
number of (meta-) agents for computational efficiency. Our findings demonstrate
that tessellations constructed according to the Voronoi Diagram of specific
location types on the street network better preserve dynamics compared to
Census Block Group tessellations and better than Euclidean-based tessellations.
Furthermore, the Voronoi Diagram tessellation and also a hybrid -- Voronoi
Diagram - and Census Block Group - based -- tessellation require fewer
meta-agents to adequately approximate full-scale dynamics. Our analysis spans a
range of city sizes in the United States, encompassing small (Santa Fe, NM),
medium (Seattle, WA), and large (Chicago, IL) urban areas. This examination
also provides valuable insights into the effects of agent count reduction,
varying sensitivity metrics, and the influence of city-specific factors
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