3,090 research outputs found
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
Crossing Roads of Federated Learning and Smart Grids: Overview, Challenges, and Perspectives
Consumer's privacy is a main concern in Smart Grids (SGs) due to the
sensitivity of energy data, particularly when used to train machine learning
models for different services. These data-driven models often require huge
amounts of data to achieve acceptable performance leading in most cases to
risks of privacy leakage. By pushing the training to the edge, Federated
Learning (FL) offers a good compromise between privacy preservation and the
predictive performance of these models. The current paper presents an overview
of FL applications in SGs while discussing their advantages and drawbacks,
mainly in load forecasting, electric vehicles, fault diagnoses, load
disaggregation and renewable energies. In addition, an analysis of main design
trends and possible taxonomies is provided considering data partitioning, the
communication topology, and security mechanisms. Towards the end, an overview
of main challenges facing this technology and potential future directions is
presented
Vertical Federated Learning:A Structured Literature Review
Federated Learning (FL) has emerged as a promising distributed learning paradigm with an added advantage of data privacy. With the growing interest in having collaboration among data owners, FL has gained significant attention of organizations. The idea of FL is to enable collaborating participants train machine learning (ML) models on decentralized data without breaching privacy. In simpler words, federated learning is the approach of ``bringing the model to the data, instead of bringing the data to the mode''. Federated learning, when applied to data which is partitioned vertically across participants, is able to build a complete ML model by combining local models trained only using the data with distinct features at the local sites. This architecture of FL is referred to as vertical federated learning (VFL), which differs from the conventional FL on horizontally partitioned data. As VFL is different from conventional FL, it comes with its own issues and challenges. In this paper, we present a structured literature review discussing the state-of-the-art approaches in VFL. Additionally, the literature review highlights the existing solutions to challenges in VFL and provides potential research directions in this domain
Enhancing Scalability and Reliability in Semi-Decentralized Federated Learning With Blockchain: Trust Penalization and Asynchronous Functionality
The paper presents an innovative approach to address the challenges of
scalability and reliability in Distributed Federated Learning by leveraging the
integration of blockchain technology. The paper focuses on enhancing the
trustworthiness of participating nodes through a trust penalization mechanism
while also enabling asynchronous functionality for efficient and robust model
updates. By combining Semi-Decentralized Federated Learning with Blockchain
(SDFL-B), the proposed system aims to create a fair, secure and transparent
environment for collaborative machine learning without compromising data
privacy. The research presents a comprehensive system architecture,
methodologies, experimental results, and discussions that demonstrate the
advantages of this novel approach in fostering scalable and reliable SDFL-B
systems.Comment: To appear in 2023 IEEE Ubiquitous Computing, Electronics & Mobile
Communication Conference (IEEE UEMCON
Learning structure and schemas from heterogeneous domains in networked systems: a survey
The rapidly growing amount of available digital documents of various formats and the possibility to access these through internet-based technologies in distributed environments, have led to the necessity to develop solid methods to properly organize and structure documents in large digital libraries and repositories. Specifically, the extremely large size of document collections make it impossible to manually organize such documents. Additionally, most of the document sexist in an unstructured form and do not follow any schemas. Therefore, research efforts in this direction are being dedicated to automatically infer structure and schemas. This is essential in order to better organize huge collections as well as to effectively and efficiently retrieve documents in heterogeneous domains in networked system. This paper presents a survey of the state-of-the-art methods for inferring structure from documents and schemas in networked environments. The survey is organized around the most important application domains, namely, bio-informatics, sensor networks, social networks, P2Psystems, automation and control, transportation and privacy preserving for which we analyze the recent developments on dealing with unstructured data in such domains.Peer ReviewedPostprint (published version
- …