martFL: Enabling Utility-Driven Data Marketplace with a Robust and Verifiable Federated Learning Architecture

The development of machine learning models requires a large amount of training data. Data marketplaces are essential for trading high-quality, private-domain data not publicly available online. However, due to growing data privacy concerns, direct data exchange is inappropriate. Federated Learning (FL) is a distributed machine learning paradigm that exchanges data utilities (in form of local models or gradients) among multiple parties without directly sharing the raw data. However, several challenges exist when applying existing FL architectures to construct a data marketplace: (i) In existing FL architectures, Data Acquirers (DAs) cannot privately evaluate local models from Data Providers (DPs) prior to trading; (ii) Model aggregation protocols in existing FL designs struggle to exclude malicious DPs without "overfitting" to the DA's (possibly biased) root dataset; (iii) Prior FL designs lack a proper billing mechanism to enforce the DA to fairly allocate the reward according to contributions made by different DPs. To address above challenges, we propose martFL, the first federated learning architecture that is specifically designed to enable a secure utility-driven data marketplace. At a high level, martFL is powered by two innovative designs: (i) a quality-aware model aggregation protocol that achieves robust local model aggregation even when the DA's root dataset is biased; (ii) a verifiable data transaction protocol that enables the DA to prove, both succinctly and in zero-knowledge, that it has faithfully aggregates the local models submitted by different DPs according to the committed aggregation weights, based on which the DPs can unambiguously claim the corresponding reward. We implement a prototype of martFL and evaluate it extensively over various tasks. The results show that martFL can improve the model accuracy by up to 25% while saving up to 64% data acquisition cost

Digital Management of Competencies in Web 3.0: The C-Box® Approach

Management of competencies is a crucial concern for both learners and workers as well as for training institutions and companies. For the former, it allows users to track and certify the acquired skills to apply for positions; for the latter, it enables better organisation of business processes. However, currently, most software systems for competency management adopted by the industry are either organisation-centric or centralised: that is, they either lock-in students and employees wishing to export their competencies elsewhere, or they require users’ trust and for users to give up privacy (to store their personal data) while being prone to faults. In this paper, we propose a user-centric, fully decentralised competency management system enabling verifiable, secure, and robust management of competencies digitalised as Open Badges via notarization on a public blockchain. This way, whoever acquires the competence or achievement retains full control over it and can disclose his/her own digital certifications only when needed and to the extent required, migrate them across storage platforms, and let anyone verify the integrity and validity of such certifications independently of any centralised organisation. The proposed solution is based on C-Box®, an existing application for the management of digital competencies that has been improved to fully support models, standards, and technologies of the so-called Web 3.0 vision—a global effort by major web organisations to “give the web back to the people”, pushing for maximum decentralisation of control and user-centric data ownership

The polysemy of the Spanish verb sentir: a behavioral profile analysis

This study investigates the intricate polysemy of the Spanish perception verb sentir (‘feel’) which, analogous to the more-studied visual perception verbs ver (‘see’) and mirar (‘look’), also displays an ample gamut of semantic uses in various syntactic environments. The investigation is based on a corpus-based behavioral profile (BP) analysis. Besides its methodological merits as a quantitative, systematic and verifiable approach to the study of meaning and to polysemy in particular, the BP analysis offers qualitative usage-based evidence for cognitive linguistic theorizing. With regard to the polysemy of sentir, the following questions were addressed: (1) What is the prototype of each cluster of senses? (2) How are the different senses structured: how many senses should be distinguished – i.e. which senses cluster together and which senses should be kept separately? (3) Which senses are more related to each other and which are highly distinguishable? (4) What morphosyntactic variables make them more or less distinguishable? The results show that two significant meaning clusters can be distinguished, which coincide with the division between the middle voice uses (sentirse) and the other uses (sentir). Within these clusters, a number of meaningful subclusters emerge, which seem to coincide largely with the more general semantic categories of physical, cognitive and emotional perception

VeriFi:Towards Verifiable Federated Unlearning

Federated learning (FL) is a collaborative learning paradigm where participants jointly train a powerful model without sharing their private data. One desirable property for FL is the implementation of the right to be forgotten (RTBF), i.e., a leaving participant has the right to request to delete its private data from the global model. However, unlearning itself may not be enough to implement RTBF unless the unlearning effect can be independently verified, an important aspect that has been overlooked in the current literature. In this paper, we prompt the concept of verifiable federated unlearning, and propose VeriFi, a unified framework integrating federated unlearning and verification that allows systematic analysis of the unlearning and quantification of its effect, with different combinations of multiple unlearning and verification methods. In VeriFi, the leaving participant is granted the right to verify (RTV), that is, the participant notifies the server before leaving, then actively verifies the unlearning effect in the next few communication rounds. The unlearning is done at the server side immediately after receiving the leaving notification, while the verification is done locally by the leaving participant via two steps: marking (injecting carefully-designed markers to fingerprint the leaver) and checking (examining the change of the global model's performance on the markers). Based on VeriFi, we conduct the first systematic and large-scale study for verifiable federated unlearning, considering 7 unlearning methods and 5 verification methods. Particularly, we propose a more efficient and FL-friendly unlearning method, and two more effective and robust non-invasive-verification methods. We extensively evaluate VeriFi on 7 datasets and 4 types of deep learning models. Our analysis establishes important empirical understandings for more trustworthy federated unlearning

A Descriptive Study on Digital Innovations and Technologies in Libraries

Libraries are social institutions that adopt new innovations for enhancing service and quality. This paper is about the descriptive study of three major technologies such as Artificial Intelligence, Big Data, BlockChain and their application in libraries and information centers. This paper also investigates, Libtech, an innovative open platform in Iran by merging these technologies. The adoption of new technologies will help the libraries to be up to date in quality and it also improves the user experience