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A Tutorial on Federated Learning from Theory to Practice:Foundations,Software Frameworks,Exemplary Use cases,and Selected Trends

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IEEE/cAA Journal of Automatica Sinica 2024年第4期11卷 824-850页

作者： M.Victoria Luzón Nuria Rodríguez-Barroso Alberto Argente-Garrido Daniel Jiménez-López Jose M.Moyano Javier Del Ser Weiping Ding Francisco Herrera Department of Software Engineering Andalusian Research Institute in Data Science and Computational Intelligence(DaSCI)University of GranadaGranada 18071Spain Department of Computer Science and Artificial Intelligence Andalusian Research Institute in Data Science and Computational Intelligence(DaSCI)University of GranadaGranada 18071Spain Department of Communications Engineering University of the Basque Country(UPV/EHU)and also with TECNALIABasque Research&Technology Alliance(BRTA)Spain School of Information Science and Technology Nantong UniversityNantong 226019China

When data privacy is imposed as a necessity,Federated learning(FL)emerges as a relevant artificial intelligence field for developing machine learning(ML)models in a distributed and decentralized *** allows ML models to be trained on local devices without any need for centralized data transfer,thereby reducing both the exposure of sensitive data and the possibility of data interception by malicious third *** paradigm has gained momentum in the last few years,spurred by the plethora of real-world applications that have leveraged its ability to improve the efficiency of distributed learning and to accommodate numerous participants with their data *** virtue of FL,models can be learned from all such distributed data sources while preserving data *** aim of this paper is to provide a practical tutorial on FL,including a short methodology and a systematic analysis of existing software ***,our tutorial provides exemplary cases of study from three complementary perspectives:i)Foundations of FL,describing the main components of FL,from key elements to FL categories;ii)Implementation guidelines and exemplary cases of study,by systematically examining the functionalities provided by existing software frameworks for FL deployment,devising a methodology to design a FL scenario,and providing exemplary cases of study with source code for different ML approaches;and iii)Trends,shortly reviewing a non-exhaustive list of research directions that are under active investigation in the current FL *** ultimate purpose of this work is to establish itself as a referential work for researchers,developers,and data scientists willing to explore the capabilities of FL in practical applications.

关键词： data privacy distributed machine learning federated learning software frameworks

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Past, present, and future of social commerce: a bibliometric analysis

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Quality and Quantity 2025年 1-33页

作者： Herzallah, Doaa Liébana-cabanillas, Francisco Muñoz-Leiva, Francisco Zarco, carmen Department of Marketing and Market Research University of Granada Granada Spain Andalusian Institute of Research and Innovation in Tourism (IATUR) Granada Spain Andalusian Research Institute in Data Science and Computational Intelligence (DASCI) Granada Spain

Given the significance of social commerce, extensive research has been conducted from various perspectives, resulting in numerous reviews and meta-analytical papers. However, these previous studies have highlighted information gaps and variations in approaches. Therefore, there is a need for a comprehensive examination of social commerce research, encompassing past, present, and future themes, as well as an investigation into additional features of social commerce. To address this need, the present study undertakes a comprehensive thematic review of social commerce research for two distinct periods: 2008–2018 and 2019–2022. Bibliometric analyses were conducted using data extracted from the Web of science database. The research employed science mapping techniques through the utilization of SciMAT and VOSviewer software. The findings of this study identified several themes related to social commerce during the first period, including product, perceived sociability, impulse buying, information technology, motivation, mobile commerce, and others, which were extensively explored. In the second period, themes such as UTAUT, SMEs, perceived risk, live stream, SOR (stimulus-organism-response), and the concept swift guanxi received significant attention. These findings, among others, contribute to the evolving understanding of the social commerce concept. By conducting a comprehensive thematic review and employing bibliometric analyses, this research fills existing knowledge gaps and provides a broader understanding of social commerce research across different periods. The findings shed light on the evolution of social commerce and pave the way for future investigations in this field. © The Author(s) 2025.

关键词： Bibliometric study co-word analysis Social commerce

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Toward Responsible Artificial intelligence Systems: Safety and Trustworthiness 8th

Toward Responsible Artificial Intelligence Systems: Safety a...

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8th International conference on Engineering of computer-Based Systems, EcBS 2023

作者： Herrera, Francisco Department Computer Sciences and Artificial Intelligence Andalusian Research Institute on Data Science and Computational Intelligence University of Granada Granada Spain

ISBN: (纸本)9783031492518

This short paper associated to the invited lectures introduces two key concepts essential to artificial intelligence (AI), the area of trustworthy AI and the concept of responsible AI systems, fundamental to understand the technological, ethical and legal context of the current framework of debate and regulation of AI. The aim is to understand their dimension and their interrelation with the rest of the elements involved in the regulation and auditability of AI algorithms in order to achieve safe and trusted AI. We highlight concepts in bold in order to fix the moment when they are described in context. © 2024, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Artificial intelligence

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A Large-Scale Group Decision-Making Approach Employing Large Language Models to Detect Assertive Groups 23

A Large-Scale Group Decision-Making Approach Employing Large...

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23rd International conference on New Trends in Intelligent Software Methodologies, Tools and Techniques, SoMeT 2024

作者： carbó-García, Santiago Trillo, José Ramón Tapia, Juan Miguel Pérez, Ignacio Javier Herrera-Viedma, Enrique cabrerizo, Francisco Javier Andalusian Research Institute in Data Science and Computational Intelligence Department of Computer Science and Artificial Intelligence University of Granada Granada Spain Department of Quantitative Methods for Business and Economics University of Granada Granada Spain

ISBN: (纸本)9781643685380

Large-Scale Group Decision-Making, propelled by the advent of Large Language Models and the imperative for assertiveness in decision-making processes, emerges as a pivotal area of research. This paper navigates through the landscape of Large-Scale Group Decision-Making, delineating its significance across diverse domains, including social networks and e-democracy. Amidst its nascent status, Large-Scale Group Decision-Making encounters formidable challenges, particularly in information management and fostering consensus among a multitude of experts. This paper aims to illuminate the goals and hurdles facing Large-Scale Group Decision-Making approaches through an exhaustive review of contemporary literature and methodologies. By confronting these challenges head-on, Large-Scale Group Decision-Making holds the potential to redefine decision-making paradigms, bolster assertiveness, and elevate collective problem-solving capabilities in an increasingly interconnected world. © 2024 IOS Press. All rights reserved.

关键词： Information management

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An Increasing Assessment theof dependability Different Reinforcement of critical safety Learning systems Methods through for creating aReinforcement Decision Support learning Systemwith Based Petri onnet themodel Petri Net Model

An Increasing Assessment theof dependability Different Reinf...

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2022 Annual conference of the Prognostics and Health Management Society, PHM 2022

作者： Saleh, Ali chiachio, Manuel Andalusian Research Institute in Data Science and Computational Intelligence University of Granada Granada18071 Spain

ISBN: (纸本)9781936263370

Infrastructure safety, operation, and management are directly affected by operation strategy which directly influence the life cycle and have a tremendous impact on operation costs. Management strategies can be branched to unrelated decisions which are typically difficult to be formulated mathematically. Reinforcement Learning serves as an adequate tool to account for unrelated decisions and optimize them in accordance with a final goal. Besides, it allows for a strategy to be flexible by autonomously changing with the variation of the system condition without the need for any user intervention. On the other hand, Petri nets are a suitable tool for maintenance modeling, as they can deal with heterogeneous information, parallel operations, and synchronization, and provide a graphical interpretation for the processes. Also, they allow for formulating RL problems based on their standard components (namely, places and transitions) which makes them suitable for forming a hybrid tool with the RL. In this work, an intelligent tool for modeling and optimizing maintenance has been developed by combining high-level Petri net (HLPN) and Reinforcement Learning (RL). RL actions are modeled by creating a special type of PN transitions, and rewards are given in terms of the running revenues and costs. The method was used to model a safety-critical system (ScS) and optimize its maintenance schedule in order to avoid the catastrophic consequences of failures while reducing the running costs. The results show that the proposed approach is capable of providing an intelligent management tool for ScS operation under flexible, yet unveiled policies, and also allows assessment of the safety of the system based on the simulated system states. © 2022 Prognostics and Health Management Society. All rights reserved.

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LSTM new gate for computing the efficiency on inputdata

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Knowledge-Based Systems 2025年 322卷

作者： García cabello, Julia carbó-García, Santiago Department of Applied Mathematics Andalusian Research Institute in Data Science and Computational Intelligence University of Granada Granada Spain Department of Computer Science and Artificial Intelligence Andalusian Research Institute in Data Science and Computational Intelligence University of Granada Granada Spain

The recognized learning ability of neural networks (NNs) is determined by their training process. The NN data-dependent nature makes that their success depends to a large extent on the quality of the training data sets. This paper addresses the selection of the best training sets for Long Short Time Models (LSTMs) in real-life contexts. If such selection from a theoretical standpoint is already complex, it becomes challenging when addressed for real scenarios, where effectiveness should be replaced by efficiency (i.e., effectiveness in the shortest possible time and without increasing the computational cost). Our proposal revolves around the introduction, definition and structuring of a new LSTM gate: the efficiency gate, that applies to those inputs that have passed the forget gate. The efficiency gate is responsible for ensuring that training advances only with the best inputs in the sense that the corresponding outputs match as closely as possible with a target value (best estimators). For this purpose, new mathematical results will be demonstrated here in order to prove that our efficiency gate is mathematically feasible. We also fully describe how the new gate joins the LSTM operation, blending in with it. The consequence of such natural integration is that the computational effort is practically unchanged. Our methodology also allows the user to set the admissible error thresholds (which are different in each real scenario) as part of the setting options. © 2025 The Authors

关键词： Efficiency gate New LSTM gate Selection of training data sets Real-life contexts

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Reviving stagnated debates in Group Decision Making environments with high number of alternatives 10

Reviving stagnated debates in Group Decision Making environm...

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10th International conference on Information Technology and Quantitative Management, ITQM 2023

作者： Morente-Molinera, J.A. Barragán-Guzmán, M. Trillo, J.R. Martínez-Sánchez, M.A. cabrerizo, F.J. Herrera-Viedma, E. Andalusian Research Institute in Data Science and Computational Intelligence University of Granada Granada Spain Department of Social Work and Social Services University of Granada Granada18001 Spain

In group decision-making, experts try to obtain a consensus to determine how to order a series of alternatives. A consensus is a decision that reflects the opinions of every group member. consensus requires discussion and deliberation between the group members. During the process, it is normal that the discussion process to stagnate. In this article, we present a new method of group decision-making that solves the stagnation of the process by including new information. The timing of this is determined by a process stagnation analysis. Fuzzy Ontologies allow experts to work in environments with large numbers of alternatives. The method takes into account the experts' rankings of alternatives to determine which criteria the experts seem to like the most. When introducing new information into the debate, experts are given the possibility to explore alternatives that are very different from those already seen or to look for alternatives that meet the criteria that are preferred the most. © 2023 The Authors. Published by Elsevier B.V. This is an open access article under the cc BY-Nc-ND license (https://***/licenses/by-nc-nd/4.0) Peer-review under responsibility of the scientific committee of the Tenth International conference on Information Technology and Quantitative Management.

关键词： Ontology

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A Web Group Decision Support System Based on Granular computing 21

A Web Group Decision Support System Based on Granular Comput...

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21st International conference on New Trends in Intelligent Software Methodologies, Tools and Techniques, SoMeT 2022

作者： cabrerizo, Francisco Javier González-Quesada, Juan carlos Pérez, Ignacio Javier cobo, Manuel Jesús Herrera-Viedma, Enrique Department of Computer Science and Artificial Intelligence Andalusian Research Institute in Data Science and Computational Intelligence DaSCI University of Granada Granada18071 Spain

ISBN: (纸本)9781643683164

Achieving a good agreement level between decision-makers is a crucial point to solve group decision-making processes. Habitually, it is the duty of the moderator, a person in charge of assuring that the consensus reaching process is conducted correctly. This person also offers advice to the decision-makers with the objective that they modify their assessments and narrow their differences. A great number of theoretical models have been introduced to help or substitute the moderator's tasks. However, few of them have been implemented in practice. In this study, we present a web group decision support system helping, and even substituting, the moderator's functions during the whole decision-making process. This system is based on the granular computing paradigm to improve both the consistency and the consensus achieved between the decision-makers. In addition, the system improves them with the possible minimum adjustment, i.e., it tries to modify the decision-makers' assessments as minimum as possible. This group decision support system provides a web interface allowing to conduct group decision-making processes in which the decision-makers do not have the choice to meet together physically. © 2022 The authors and IOS Press. All rights reserved.

关键词： Decision support systems

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Building consensus in Group Decision-Making with Intuitionistic Reciprocal Preference Relations: An Analysis of Various Protocols of Information Granularity Distribution

Building Consensus in Group Decision-Making with Intuitionis...

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2024 IEEE International conference on Systems, Man, and cybernetics, SMc 2024

作者： Gonzalez-Quesada, Juan carlos cabrerizo, Francisco Javier Herrera-Viedma, Enrique Pedrycz, Witold Andalusian Research Institute in Data Science and Computational Intelligence DaSCI University of Granada Department of Computer Science and Artificial Intelligence Granada18071 Spain University of Alberta Department of Electrical & Computer Engineering EdmontonABT6R 2V4 Canada

ISBN: (纸本)9781665410205

On the one hand, to model experts' preferences in group decision-making, intuitionistic reciprocal preference relations have widely been used because they allow for accommodating hesitation degrees, which are inherent to all decision-making processes. On the other hand, an optimization of information granularity distribution has recently been applied to establish consensus during group decision-making processes. concretely, a symmetric and uniform distribution of information granularity has been considered for intuitionistic reciprocal preference relations. However, there exist other protocols of information granularity distribution that could be used. Therefore, we aim to analyze all the information granularity distribution protocols and determine their effectiveness in building consensus through intuitionistic reciprocal preference relations. The performance of the different protocols is discussed by conducting some numerical experiments that help provide insights into the effectiveness of the protocols to build consensus. © 2024 IEEE.

关键词： Information granules

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Large-scale group decision-making method based on knowledge differentiation and the grouping of alternatives into modifiable scenarios 9th

Large-scale group decision-making method based on knowledge ...

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9th International conference on Information Technology and Quantitative Management, ITQM 2022

作者： Trillo, José Ramón Herrera-Viedma, Enrique Higueras-Ruiz, María José Alonso, Sergio Morente-Molinera, Juan Antonio cabrerizo, Francisco Javier Department of Computer Science and Artificial Intelligence Andalusian Research Institute of Data Science and Computational Intelligence DaSCI University of Granada Granada18071 Spain Department of Information and Communication University of Granada Granada18071 Spain Department of Software Engineering Andalusian Research Institute in Data Science and Computational Intelligence DaSCI University of Granada Granada18071 Spain

Nowadays, group decision-making is an everyday event, since we usually live in a community. Nevertheless, when the number of participants is large, it starts to generate a lot of information that is complex to manage, and unlike group decision-making, where the participants are usually experts, when the number of participants increases, not all of them are, which generates a conflict of interest because the value of the vote of an expert and a (non-expert) user is worth the same. Moreover, it may happen that not all participants who start a process will finish it and not all of them will be able to make all the assessments, since they do not know all the options. Therefore, to solve all the drawbacks, this paper presents a large-scale group decision-making system in a modifiable scenario environment that groups participants according to their preferred alternative and also performs knowledge differentiation, clustering them into experts and users. This allows for optimising the consensus process, as the consensus is made by comparing the clusters and the participants with each other. © 2022 The Authors. Published by Elsevier B.V.

关键词： Decision making

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