2,102 research outputs found
Systematizing Decentralization and Privacy: Lessons from 15 Years of Research and Deployments
Decentralized systems are a subset of distributed systems where multiple
authorities control different components and no authority is fully trusted by
all. This implies that any component in a decentralized system is potentially
adversarial. We revise fifteen years of research on decentralization and
privacy, and provide an overview of key systems, as well as key insights for
designers of future systems. We show that decentralized designs can enhance
privacy, integrity, and availability but also require careful trade-offs in
terms of system complexity, properties provided, and degree of
decentralization. These trade-offs need to be understood and navigated by
designers. We argue that a combination of insights from cryptography,
distributed systems, and mechanism design, aligned with the development of
adequate incentives, are necessary to build scalable and successful
privacy-preserving decentralized systems
An integrated framework for user modeling using deep learning on a data monetization platform
This paper presents a novel approach to privacy-preserving user modeling for digital marketing campaigns using deep learning techniques on a data monetization platform, which enables users to maintain control over their personal data while allowing marketers to identify suitable target audiences for their campaigns. The system comprises of several stages, starting with the use of representation learning on hyperbolic space to capture the latent user interests across multiple data sources with hierarchical structures. Next, Generative Adversarial Networks are employed to generate synthetic user interests from these embeddings. To ensure the privacy of user data, a Federated Learning technique is implemented for decentralized user modeling training, without sharing data with marketers. Lastly, a targeting strategy based on recommendation system is constructed to leverage the learned user interests for identifying the optimal target audience for digital marketing campaigns. Overall, the proposed approach provides a comprehensive solution for privacy-preserving user modeling for digital marketing.publishersversionpublishe
Trustworthy Edge Machine Learning: A Survey
The convergence of Edge Computing (EC) and Machine Learning (ML), known as
Edge Machine Learning (EML), has become a highly regarded research area by
utilizing distributed network resources to perform joint training and inference
in a cooperative manner. However, EML faces various challenges due to resource
constraints, heterogeneous network environments, and diverse service
requirements of different applications, which together affect the
trustworthiness of EML in the eyes of its stakeholders. This survey provides a
comprehensive summary of definitions, attributes, frameworks, techniques, and
solutions for trustworthy EML. Specifically, we first emphasize the importance
of trustworthy EML within the context of Sixth-Generation (6G) networks. We
then discuss the necessity of trustworthiness from the perspective of
challenges encountered during deployment and real-world application scenarios.
Subsequently, we provide a preliminary definition of trustworthy EML and
explore its key attributes. Following this, we introduce fundamental frameworks
and enabling technologies for trustworthy EML systems, and provide an in-depth
literature review of the latest solutions to enhance trustworthiness of EML.
Finally, we discuss corresponding research challenges and open issues.Comment: 27 pages, 7 figures, 10 table
Decentralized Federated Learning: Fundamentals, State-of-the-art, Frameworks, Trends, and Challenges
In the last decade, Federated Learning (FL) has gained relevance in training
collaborative models without sharing sensitive data. Since its birth,
Centralized FL (CFL) has been the most common approach in the literature, where
a central entity creates a global model. However, a centralized approach leads
to increased latency due to bottlenecks, heightened vulnerability to system
failures, and trustworthiness concerns affecting the entity responsible for the
global model creation. Decentralized Federated Learning (DFL) emerged to
address these concerns by promoting decentralized model aggregation and
minimizing reliance on centralized architectures. However, despite the work
done in DFL, the literature has not (i) studied the main aspects
differentiating DFL and CFL; (ii) analyzed DFL frameworks to create and
evaluate new solutions; and (iii) reviewed application scenarios using DFL.
Thus, this article identifies and analyzes the main fundamentals of DFL in
terms of federation architectures, topologies, communication mechanisms,
security approaches, and key performance indicators. Additionally, the paper at
hand explores existing mechanisms to optimize critical DFL fundamentals. Then,
the most relevant features of the current DFL frameworks are reviewed and
compared. After that, it analyzes the most used DFL application scenarios,
identifying solutions based on the fundamentals and frameworks previously
defined. Finally, the evolution of existing DFL solutions is studied to provide
a list of trends, lessons learned, and open challenges
Trustworthy Federated Learning: A Survey
Federated Learning (FL) has emerged as a significant advancement in the field
of Artificial Intelligence (AI), enabling collaborative model training across
distributed devices while maintaining data privacy. As the importance of FL
increases, addressing trustworthiness issues in its various aspects becomes
crucial. In this survey, we provide an extensive overview of the current state
of Trustworthy FL, exploring existing solutions and well-defined pillars
relevant to Trustworthy . Despite the growth in literature on trustworthy
centralized Machine Learning (ML)/Deep Learning (DL), further efforts are
necessary to identify trustworthiness pillars and evaluation metrics specific
to FL models, as well as to develop solutions for computing trustworthiness
levels. We propose a taxonomy that encompasses three main pillars:
Interpretability, Fairness, and Security & Privacy. Each pillar represents a
dimension of trust, further broken down into different notions. Our survey
covers trustworthiness challenges at every level in FL settings. We present a
comprehensive architecture of Trustworthy FL, addressing the fundamental
principles underlying the concept, and offer an in-depth analysis of trust
assessment mechanisms. In conclusion, we identify key research challenges
related to every aspect of Trustworthy FL and suggest future research
directions. This comprehensive survey serves as a valuable resource for
researchers and practitioners working on the development and implementation of
Trustworthy FL systems, contributing to a more secure and reliable AI
landscape.Comment: 45 Pages, 8 Figures, 9 Table
- …