1,434 research outputs found
Federated Analytics for 6G Networks:Applications, Challenges, and Opportunities
Extensive research is underway to meet the hyper-connectivity demands of 6G networks, driven by applications like XR/VR and holographic communications, which generate substantial data requiring network-based processing, transmission, and analysis. However, adhering to diverse data privacy and security policies in the anticipated multi-domain, multi-tenancy scenarios of 6G presents a significant challenge. Federated Analytics (FA) emerges as a promising distributed computing paradigm, enabling collaborative data value generation while preserving privacy and reducing communication overhead. Using big data principles, FA can be applied to manage and secure distributed heterogeneous networks, improving performance, reliability, visibility, and security without compromising data confidentiality. This paper provides a comprehensive overview of potential FA applications, domains, and types in 6G networks, elucidating analysis methods, techniques, and queries. It explores complementary approaches to enhance privacy and security in 6G networks alongside FA and discusses the challenges and prerequisites for successful FA implementation. Additionally, distinctions between FA and Federated Learning are drawn, highlighting their synergistic potential through a network orchestration scenario
Peer-to-Peer Networks and Computation: Current Trends and Future Perspectives
This research papers examines the state-of-the-art in the area of P2P networks/computation. It attempts to identify the challenges that confront the community of P2P researchers and developers, which need to be addressed before the potential of P2P-based systems, can be effectively realized beyond content distribution and file-sharing applications to build real-world, intelligent and commercial software systems. Future perspectives and some thoughts on the evolution of P2P-based systems are also provided
A Taxonomy of Data Grids for Distributed Data Sharing, Management and Processing
Data Grids have been adopted as the platform for scientific communities that
need to share, access, transport, process and manage large data collections
distributed worldwide. They combine high-end computing technologies with
high-performance networking and wide-area storage management techniques. In
this paper, we discuss the key concepts behind Data Grids and compare them with
other data sharing and distribution paradigms such as content delivery
networks, peer-to-peer networks and distributed databases. We then provide
comprehensive taxonomies that cover various aspects of architecture, data
transportation, data replication and resource allocation and scheduling.
Finally, we map the proposed taxonomy to various Data Grid systems not only to
validate the taxonomy but also to identify areas for future exploration.
Through this taxonomy, we aim to categorise existing systems to better
understand their goals and their methodology. This would help evaluate their
applicability for solving similar problems. This taxonomy also provides a "gap
analysis" of this area through which researchers can potentially identify new
issues for investigation. Finally, we hope that the proposed taxonomy and
mapping also helps to provide an easy way for new practitioners to understand
this complex area of research.Comment: 46 pages, 16 figures, Technical Repor
Supporting the Reuse of Open Educational Resources through Open Standards
Glahn, C., Kalz, M., Gruber, M., & Specht, M. (2010). Supporting the Reuse of Open Educational Resources through Open Standards. In T. Hirashima, A. F. Mohd Ayub, L. F. Kwok, S. L. Wong, S. C. Kong, & F. Y. Yu (Eds.), Workshop Proceedings of the 18th International Conference on Computers in Education: ICCE2010 (pp. 308-315). November, 29 - December, 3, 2010, Putrajaya, Malaysia: Asia-Pacific Society for Computers in Education.In this paper we analyse open standards for supporting the reuse of OER in different knowledge domains based on a generic architecture for content federation and higher-order services. Plenty OER are available at different institutions. We face the problem that the mere availability of these resources does not directly lead to their reuse. To increase the accessibility we integrated existing resource repositories to allow educational practitioners to discover appropriate resources. On top of this content federation we build higher order services to allow re-authoring and sharing of resources. Open standards play an important role in this process for developing high-level services for lowering the thresholds for the creation, distribution and reuse of OER in higher education.This paper has been partly sponsored by the GRAPPLE project (www.grapple-project.org) that is funded by the European Union within the Framework Programme 7 and the following European Projects funded in the eContentPlus Programme: MACE (ECP-2005-EDU-038098, portal.mace-orject.org), OpenScout (grant ECP-2008-EDU-428016, cf. www.openscout.net), and Share.TEC (ECP-2007-EDU-427015/Share.TEC, www.share-tec.eu)
Tunable Security for Deployable Data Outsourcing
Security mechanisms like encryption negatively affect other software quality characteristics like efficiency. To cope with such trade-offs, it is preferable to build approaches that allow to tune the trade-offs after the implementation and design phase. This book introduces a methodology that can be used to build such tunable approaches. The book shows how the proposed methodology can be applied in the domains of database outsourcing, identity management, and credential management
FLIPS: Federated Learning using Intelligent Participant Selection
This paper presents the design and implementation of FLIPS, a middleware
system to manage data and participant heterogeneity in federated learning (FL)
training workloads. In particular, we examine the benefits of label
distribution clustering on participant selection in federated learning. FLIPS
clusters parties involved in an FL training job based on the label distribution
of their data apriori, and during FL training, ensures that each cluster is
equitably represented in the participants selected. FLIPS can support the most
common FL algorithms, including FedAvg, FedProx, FedDyn, FedOpt and FedYogi. To
manage platform heterogeneity and dynamic resource availability, FLIPS
incorporates a straggler management mechanism to handle changing capacities in
distributed, smart community applications. Privacy of label distributions,
clustering and participant selection is ensured through a trusted execution
environment (TEE). Our comprehensive empirical evaluation compares FLIPS with
random participant selection, as well as two other "smart" selection mechanisms
- Oort and gradient clustering using two real-world datasets, two different
non-IID distributions and three common FL algorithms (FedYogi, FedProx and
FedAvg). We demonstrate that FLIPS significantly improves convergence,
achieving higher accuracy by 17 - 20 % with 20 - 60 % lower communication
costs, and these benefits endure in the presence of straggler participants
Container-based Virtual Elastic Clusters
[EN] eScience demands large-scale computing clusters to support the efficient execution of resource-intensive
scientific applications. Virtual Machines (VMs) have introduced the ability to provide customizable execution
environments, at the expense of performance loss for applications. However, in recent years, containers
have emerged as a light-weight virtualization technology compared to VMs. Indeed, the usage
of containers for virtual clusters allows better performance for the applications and fast deployment of
additional working nodes, for enhanced elasticity. This paper focuses on the deployment, configuration
and management of Virtual Elastic computer Clusters (VEC) dedicated to process scientific workloads.
The nodes of the scientific cluster are hosted in containers running on bare-metal machines. The opensource
tool Elastic Cluster for Docker (EC4Docker) is introduced, integrated with Docker Swarm to create
auto-scaled virtual computer clusters of containers across distributed deployments. We also discuss the
benefits and limitations of this solution and analyse the performance of the developed tools under a real
scenario by means of a scientific use case that demonstrates the feasibility of the proposed approach.This work has been developed under the support of the program "Ayudas para la contratacion de personal investigador en formacion de catheter predoctoral, programa VALi+d", grant number ACIF/2013/003, from the Conselleria d'Educacio of the Generalitat Valenciana. The authors wish to thank the financial support received form The Spanish Ministry of Economy and Competitiveness to develop the project "CLUVIEM", with reference TIN2013-44390-R.Alfonso Laguna, CD.; Calatrava Arroyo, A.; Moltó, G. (2017). Container-based Virtual Elastic Clusters. Journal of Systems and Software. 127:1-11. https://doi.org/10.1016/j.jss.2017.01.007S11112
Mitigating Communications Threats in Decentralized Federated Learning through Moving Target Defense
The rise of Decentralized Federated Learning (DFL) has enabled the training
of machine learning models across federated participants, fostering
decentralized model aggregation and reducing dependence on a server. However,
this approach introduces unique communication security challenges that have yet
to be thoroughly addressed in the literature. These challenges primarily
originate from the decentralized nature of the aggregation process, the varied
roles and responsibilities of the participants, and the absence of a central
authority to oversee and mitigate threats. Addressing these challenges, this
paper first delineates a comprehensive threat model, highlighting the potential
risks of DFL communications. In response to these identified risks, this work
introduces a security module designed for DFL platforms to counter
communication-based attacks. The module combines security techniques such as
symmetric and asymmetric encryption with Moving Target Defense (MTD)
techniques, including random neighbor selection and IP/port switching. The
security module is implemented in a DFL platform called Fedstellar, allowing
the deployment and monitoring of the federation. A DFL scenario has been
deployed, involving eight physical devices implementing three security
configurations: (i) a baseline with no security, (ii) an encrypted
configuration, and (iii) a configuration integrating both encryption and MTD
techniques. The effectiveness of the security module is validated through
experiments with the MNIST dataset and eclipse attacks. The results indicated
an average F1 score of 95%, with moderate increases in CPU usage (up to 63.2%
+-3.5%) and network traffic (230 MB +-15 MB) under the most secure
configuration, mitigating the risks posed by eavesdropping or eclipse attacks
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