1,720,976 research outputs found
Federated Learning Meets Blockchain: a Power Consumption Case Study
Get PDFFederated learning (FL) is emerging as the most promising approach to collaboratively train a machine learning (ML) model on a common task without centralizing data. During each FL round, participants locally train a partial model with its on-premises data. Such models are subsequently aggregated to derive a global one. How these partial models are combined is a primary concern. Traditional approaches usually rely on a parameter server that introduces many weaknesses such as single point of failure, lack of trustworthiness among unknown participants, and incapacity to handle the traffic generated from millions of devices. Thus, to overcome such concerns, blockchain has recently been proposed as a valuable solution to improve the robustness of FL approaches. The full-blown benefits of using blockchain enable tackling the limits of centralized servers. However, energy consumption is still one of the significant factors inhibiting its widespread due to the current discussions on climate change and sustainability. Recently, a growing number of research works have been focusing on integrating FL and blockchain, nevertheless, adequate analysis and estimate of their energy and power consumption are often lacking. This paper presents an estimate of the power consumption of FlowChain, an architecture that integrates FL with blockchain to simplify the use of FL. Experimental results demonstrate that the overall power consumption significantly depends on the ML model adopted
A Fully Decentralized Architecture for Access Control Verification in Serverless Environments
No full textServerless computing is a novel paradigm that has been widely adopted, in recent years, across many sectors due to its fine-grained scalability and fast time-to-market. This paradigm aims at offloading users from heavy burden tasks including those related to authentication and authorization. However, existing security mechanisms provided by cloud providers do not seem to be adequate to completely secure serverless platforms. In particular, typical access control solutions rely either on centralized authorization services or implement access control verification within the business logic. These approaches respectively degrade system performance and lead to security issues derived from the tight coupling among code and authorization verification. In this paper, we present a solution to address these problems with a fully decentralized architecture integrating access control verification in serverless environments. We implemented a prototype of the proposed architecture and evaluated its performance under different load conditions. Experiments show that our proposal outperforms other approaches
L’eterno presente. Considerazioni sull’evoluzione plastica della scultura e del disegno in alcuni periodi della vita artistica di Vincenzo Gemito
No full textIl contributo ripercorre le maggiori tappe della vita artistica del grande scultore e disegnatore Vincenzo Gemito (Napoli, 1852-1929), fra i maggiori protagonisti del panorama delle arti nazionale e internazionale. Nel saggio sono messi a fuoco i nessi principali della sua produzione e le linee principali della sua poetica, e precisati molti dati relativi alla sua vicenda biografica e alle opere. Sono altresì pubblicati alcuni disegni inediti
Enabling Federated Learning at the Edge through the IOTA Tangle
Get PDFThe proliferation of Internet of Things (IoT) devices, generating massive amounts of heterogeneous distributed
data, has pushed toward edge cloud computing as a promising paradigm to bring cloud capabilities closer to
data sources. In many cases of practical interest, centralized Machine Learning (ML) approaches can hardly be
employed due to high communication costs, low reliability, legal restrictions, and scalability issues. Therefore,
Federated Learning (FL) is emerging as a promising distributed ML approach that enables models to be trained
on remote devices using their local data. However, ‘‘traditional’’ FL solutions still present open technical
challenges, such as single points of failure and lack of trustworthiness among participants. To address these
open challenges, some researchers have started to propose leveraging blockchain technologies. However, the
adoption of blockchain for FL at the edge is limited by several factors nowadays, such as long waiting times
for transaction confirmation and high energy consumption.
In this work, we conduct an original and comprehensive analysis of the key design challenges to address
towards an efficient implementation of FL at the edge, and analyze how Distributed Ledger Technologies
(DLTs) can be employed to overcome them. Then, we present a novel architecture that enables FL at the edge
by leveraging the IOTA Tangle, a next-generation DLT whose data structure is a Directed Acyclic Graph (DAG),
and the InterPlanetary File System (IPFS) to store and share partial models. Experimental results demonstrate
the feasibility and efficiency of our proposed solution in real-world deployment scenarios
Federated Unlearning: A Survey on Methods, Design Guidelines, and Evaluation Metrics
Get PDFFederated learning (FL) enables collaborative training of a machine learning (ML) model across multiple parties, facilitating the preservation of users' and institutions' privacy by maintaining data stored locally. Instead of centralizing raw data, FL exchanges locally refined model parameters to build a global model incrementally. While FL is more compliant with emerging regulations such as the European General Data Protection Regulation (GDPR), ensuring the right to be forgotten in this context - allowing FL participants to remove their data contributions from the learned model - remains unclear. In addition, it is recognized that malicious clients may inject backdoors into the global model through updates, e.g., to generate mispredictions on specially crafted data examples. Consequently, there is the need for mechanisms that can guarantee individuals the possibility to remove their data and erase malicious contributions even after aggregation, without compromising the already acquired 'good' knowledge. This highlights the necessity for novel federated unlearning (FU) algorithms, which can efficiently remove specific clients' contributions without full model retraining. This article provides background concepts, empirical evidence, and practical guidelines to design/implement efficient FU schemes. This study includes a detailed analysis of the metrics for evaluating unlearning in FL and presents an in-depth literature review categorizing state-of-the-art FU contributions under a novel taxonomy. Finally, we outline the most relevant and still open technical challenges, by identifying the most promising research directions in the field
DIVA: A DID-based reputation system for secure transmission in VANETs using IOTA
Get PDFToday's advancement in Vehicular Ad-hoc Networks (VANET) constitutes a cornerstone in ensuring traffic safety in Intelligent Transportation Systems (ITS). In this context, vehicle-to-vehicle (V2V) communications are a pivotal enabler for road safety, traffic optimization, and pedestrian protection. However, V2V communications lack effective and efficient security solutions that can adequately ensure the trustworthiness of the source of the transmitted content. In this work, we originally propose DIVA, i.e., a Decentralized Identifier-based reputation system for secure transmission in VAnets. In particular, we claim the suitability of utilizing IOTA, a Direct Acyclic Graph (DAG)-based ledger, to securely store reputation scores and of leveraging Decentralized Identifiers (DIDs) to identify participating vehicles. DIVA also incorporates and implements a reputation algorithm that computes reputation scores by analyzing both safety and non-safety messages, exchanged among vehicles and Road Side Units (RSUs) in compliance with the related European Telecommunications Standards Institute (ETSI) standards. Thus, DIVA can effectively identify malicious contributors and decrease their reputation scores. The reported experimental results clearly show the feasibility and effectiveness of DIVA, by working on an extended and comprehensive dataset of realistic V2V messages; the dataset has been made openly accessible to the research community, also to increase result reproducibility
Membership Proof in Federated Learning via Cryptographic Accumulators
Get PDFFederated Learning (FL) is a decentralized training paradigm where clients collaboratively train a Machine Learning (ML) model without outsourcing their raw data. Each participant locally trains a model utilizing their data, and these models are periodically aggregated to build a global model. In this scenario, clients may be willing to verify the inclusion of their contributions to ensure accurate and fair computation of the global model. This verification also serves to confirm that their participation has been adequately rewarded. Therefore, FL frameworks should allow clients to efficiently verify their proof of inclusion (i.e., membership proof) in the training without affecting privacy. This paper presents a protocol for efficient membership proof in FL (MPFL). Our protocol leverages cryptographic accumulators, which enable clients to verify membership proof with minimum overhead, and a smart contract deployed on a blockchain to ensure the correct generation of the global model and membership proofs. We implemented MPFL and conducted evaluations across various datasets, ML models, and varying the number of clients. The experimental findings reveal that a client only requires 96 bytes to maintain the necessary information for verifying their inclusion, achieving this task in approximately 20 ms
TruFLaaS: Trustworthy Federated Learning as a Service
Get PDFThe increasing availability of data generated by Internet of Things (IoT) and Industrial IoT (IIoT) devices, as well as privacy and law regulations, have significantly boosted the interest in collaborative machine learning (ML) approaches. In this direction, we claim federated learning (FL) as a promising ML paradigm where participants collaboratively train a global model without outsourcing on-premises data. However, setting up and using FL can be extremely costly and time consuming. To effectively promote the adoption of FL in real-world scenarios, while limiting the overhead and knowledge of the underlying technology, service providers should offer FL as a Service (FLaaS). One of the major concerns while designing an architecture that provides FLaaS is achieving trustworthiness among involved typically unknown participants. This article presents a blockchain-based architecture that achieves trustworthy FLaaS (TruFLaaS). Our solution provides trustworthiness among third-party organizations by leveraging blockchain, smart contracts, and a decentralized oracle network. Specifically, during each FL round, the service provider supplies a sample, without overlapping, of its validation set to validate all partial models submitted by clients. By doing so, poor models, which tend to degrade performance or introduce malicious backdoors, are identified and discarded. Due to the transparency of the blockchain, not changing the validation set would enable participants to forge a malicious partial model that passes the validation phase. We evaluate our approach over two well-known IIoT data sets: the reported experimental results show that TruFLaaS outperforms the state-of-the-art literature solutions in the field
Secure software development and testing: A model-based methodology
Get PDFModern industries widely rely upon software and IT services, in a context where cybercrime is rapidly spreading
in more and more sectors. Unfortunately, despite greater general awareness of security risks and the availability
of security tools that can help to cope with those risks, many organizations (especially medium/small-size ones)
still lag when it comes to building security into their services. This is mainly due to the limited security skills
of common developers/IT project managers and to the typically high costs of security procedures. In fact,
while automated tools exist to perform code analysis, vulnerability scanning, or security testing, the manual
intervention of security experts is still required not only for security analysis and design, but also to configure
and elaborate the output of the security testing tools.
In this paper, we propose a novel secure software development methodology aimed at supporting developers
from security design to security testing, suitable for integration within modern DevOps pipelines according
to a DevSecOps (or SecDevOps) approach. The proposed methodology leverages a model-based process that
enables identifying existing threats, selecting appropriate countermeasures to enforce, and verify their mitigation
effectiveness through both static assessment procedures and targeted security tests. To demonstrate our
approach’s feasibility and concretely illustrate the devised activities, we provide a step-by-step description of
the whole process concerning a containerized microservice-based application case study. In addition, we discuss
the application of the proposed methodology, in its threat modeling and security testing phases, to a well-known
vulnerable web application widely used for security training purposes, to illustrate that we can identify most of
the existing vulnerabilities and determine appropriate test plans to assess and mitigate such vulnerabilities
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