Decentralized federated learning: Fundamentals, state of the art, frameworks, trends, and challenges
In recent years, Federated Learning (FL) has gained relevance in training collaborative
models without sharing sensitive data. Since its birth, Centralized FL (CFL) has been the …
models without sharing sensitive data. Since its birth, Centralized FL (CFL) has been the …
A comprehensive review on Federated Learning for Data-Sensitive Application: Open issues & challenges
Abstract Artificial intelligence employs Machine Learning (ML) and Deep Learning (DL) to
analyze data. In both, the data is stored centrally. The data involved may be sensitive and …
analyze data. In both, the data is stored centrally. The data involved may be sensitive and …
Sentinel: An Aggregation Function to Secure Decentralized Federated Learning
C Feng, AH Celdrán, J Baltensperger… - arXiv preprint arXiv …, 2023 - arxiv.org
The rapid integration of Federated Learning (FL) into networking encompasses various
aspects such as network management, quality of service, and cybersecurity while preserving …
aspects such as network management, quality of service, and cybersecurity while preserving …
A Multifaceted Survey on Federated Learning: Fundamentals, Paradigm Shifts, Practical Issues, Recent Developments, Partnerships, Trade-Offs, Trustworthiness, and …
A Majeed, SO Hwang - IEEE Access, 2024 - ieeexplore.ieee.org
Federated learning (FL) is considered a de facto standard for privacy preservation in AI
environments because it does not require data to be aggregated in some central place to …
environments because it does not require data to be aggregated in some central place to …
DART: A Solution for Decentralized Federated Learning Model Robustness Analysis
Federated Learning (FL) has emerged as a promising approach to address privacy
concerns inherent in Machine Learning (ML) practices. However, conventional FL methods …
concerns inherent in Machine Learning (ML) practices. However, conventional FL methods …
Assessing the Sustainability and Trustworthiness of Federated Learning Models
Artificial intelligence (AI) plays a pivotal role in various sectors, influencing critical decision-
making processes in our daily lives. Within the AI landscape, novel AI paradigms, such as …
making processes in our daily lives. Within the AI landscape, novel AI paradigms, such as …
Data Quality in Edge Machine Learning: A State-of-the-Art Survey
Data-driven Artificial Intelligence (AI) systems trained using Machine Learning (ML) are
shaping an ever-increasing (in size and importance) portion of our lives, including, but not …
shaping an ever-increasing (in size and importance) portion of our lives, including, but not …
A trustworthy federated learning framework for individual device identification
IoT scenarios face cybersecurity concerns due to unauthorized devices that can
impersonate legitimate ones by using identical software and hardware configurations. This …
impersonate legitimate ones by using identical software and hardware configurations. This …
Federated Learning and AI Regulation in the European Union: Who is liable? An Interdisciplinary Analysis
H Woisetschläger, S Mertel, C Krönke, R Mayer… - arXiv preprint arXiv …, 2024 - arxiv.org
The European Union Artificial Intelligence Act mandates clear stakeholder responsibilities in
developing and deploying machine learning applications to avoid substantial fines …
developing and deploying machine learning applications to avoid substantial fines …
Designing and Implementing an Advanced Algorithm to Measure the Trustworthiness Level of Federated Learning Models
L Zumtaugwald - 2023 - zora.uzh.ch
Artificial intelligence (AI) has immersed our daily lives and assists in the decision process of
critical sectors such as medicine and law. Therefore it is now more important than ever …
critical sectors such as medicine and law. Therefore it is now more important than ever …