A systematic literature review on federated machine learning: From a software engineering perspective
Federated learning is an emerging machine learning paradigm where clients train models
locally and formulate a global model based on the local model updates. To identify the state …
locally and formulate a global model based on the local model updates. To identify the state …
Decentral and incentivized federated learning frameworks: A systematic literature review
The advent of federated learning (FL) has sparked a new paradigm of parallel and
confidential decentralized machine learning (ML) with the potential of utilizing the …
confidential decentralized machine learning (ML) with the potential of utilizing the …
[HTML][HTML] AI-driven IoT for smart health care: Security and privacy issues
I Keshta - Informatics in medicine Unlocked, 2022 - Elsevier
Abstract The Internet of Things (IoT) has recently brought the dream of a smarter world into
an accurate picture with various services and a significant amount of data. With the …
an accurate picture with various services and a significant amount of data. With the …
Developing a Decentralized AI Model Training Framework Using Blockchain Technology
This research addresses the critical challenges in traditional centralized AI model training,
focusing on data privacy, security, and the risks associated with centralized data …
focusing on data privacy, security, and the risks associated with centralized data …
Decentralized and Incentivized Federated Learning: A Blockchain-Enabled Framework Utilising Compressed Soft-Labels and Peer Consistency
Federated Learning (FL) has emerged as a powerful paradigm in Artificial Intelligence,
facilitating the parallel training of Artificial Neural Networks on edge devices while …
facilitating the parallel training of Artificial Neural Networks on edge devices while …
Privacy-preserving statistical analysis of health data using paillier homomorphic encryption and permissioned blockchain
M Ghadamyari, S Samet - … Conference on Big Data (Big Data), 2019 - ieeexplore.ieee.org
Statistical analysis of health data is an essential task in healthcare. However, existing
healthcare systems are incompatible with this critical need due to privacy restrictions. A …
healthcare systems are incompatible with this critical need due to privacy restrictions. A …
[HTML][HTML] Distributed artificial intelligence: Taxonomy, review, framework, and reference architecture
Artificial intelligence (AI) research and market have grown rapidly in the last few years, and
this trend is expected to continue with many potential advancements and innovations in this …
this trend is expected to continue with many potential advancements and innovations in this …
Secure decentralized peer-to-peer training of deep neural networks based on distributed ledger technology
A Fadaeddini, B Majidi, M Eshghi - The Journal of Supercomputing, 2020 - Springer
The accuracy and performance of deep neural network models become important issues as
the applications of deep learning increase. For example, the navigation system of …
the applications of deep learning increase. For example, the navigation system of …
Metaheuristics algorithm-based minimization of communication costs in federated learning
The Federated learning (FL) technique resolves the issue of training machine learning (ML)
techniques on distributed networks, including the huge volume of modern smart devices. FL …
techniques on distributed networks, including the huge volume of modern smart devices. FL …
Computer-aided-diagnosis as a service on decentralized medical cloud for efficient and rapid emergency response intelligence
The COVID-19 pandemic resulted in a significant increase in the workload for the
emergency systems and healthcare providers all around the world. The emergency systems …
emergency systems and healthcare providers all around the world. The emergency systems …