Journal ArticleDOI
InFeMo: Flexible Big Data Management Through a Federated Cloud System
TLDR
A novel architecture scenario based on Cloud Computing and counts on the innovative model of Federated Learning, which incorporates all the existing Cloud models with a federated learning scenario, as well as other related technologies that may have integrated use with each other, offering a novel integrated scenario.Abstract:
This paper introduces and describes a novel architecture scenario based on Cloud Computing and counts on the innovative model of Federated Learning. The proposed model is named Integrated Federated...read more
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Journal ArticleDOI
A Joint Resource Allocation, Security with Efficient Task Scheduling in Cloud Computing Using Hybrid Machine Learning Techniques
Prasanta Kumar Bal,Sudhir Kumar Mohapatra,Tapan Kumar Das,Kathiravan Srinivasan,Yuh-Chung Hu +4 more
TL;DR: A combined resource allocation security with efficient task scheduling in cloud computing using a hybrid machine learning (RATS-HM) technique is proposed to overcome problems of efficient management of resources and state-of-art techniques are compared to prove the effectiveness.
Journal ArticleDOI
Velocity-adaptive Access Scheme for MEC-assisted Platooning Networks: Access Fairness Via Data Freshness
TL;DR: In this paper, a joint optimization problem in the MEC-assisted V2I networks and a multi-objective optimization scheme to solve the problem through adjusting the minimum contention window under the IEEE 802.11 DCF mode according to the velocities of vehicles.
Journal ArticleDOI
Resource allocation of fog radio access network based on deep reinforcement learning
Jin C. Tan,Wenbo Guan +1 more
TL;DR: The objective is to maximize the average throughput of F‐RAN architecture with hybrid energy sources while satisfying the constraints of signal to noise ratio (SNR), available bandwidth, and energy harvesting.
Journal ArticleDOI
A comprehensive survey on DDoS attacks on various intelligent systems and it's defense techniques
TL;DR: This study makes an important contribution to the field of DDoS attack detection for intelligent systems, providing a comprehensive overview of the field's evolution and current status, as well as a comprehensive, synthesized, and organized summary of various perspectives, definitions, and trends in the field.
Journal ArticleDOI
ECCHSC: Computationally and Bandwidth Efficient ECC-Based Hybrid Signcryption Protocol for Secure Heterogeneous Vehicle-to-Infrastructure Communications
TL;DR: In this paper , the authors proposed an elliptic curve cryptosystem-based hybrid signcryption (ECCHSC) protocol that satisfies the security requirements (i.e., message confidentiality, message source authentication, message integrity, nonrepudiation, and identity anonymity) for heterogeneous vehicle-to-infrastructure (V2I) communications in a single logical step.
References
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Posted Content
Communication-Efficient Learning of Deep Networks from Decentralized Data
TL;DR: This work presents a practical method for the federated learning of deep networks based on iterative model averaging, and conducts an extensive empirical evaluation, considering five different model architectures and four datasets.
Proceedings ArticleDOI
Privacy-Preserving Deep Learning
Reza Shokri,Vitaly Shmatikov +1 more
TL;DR: This paper presents a practical system that enables multiple parties to jointly learn an accurate neural-network model for a given objective without sharing their input datasets, and exploits the fact that the optimization algorithms used in modern deep learning, namely, those based on stochastic gradient descent, can be parallelized and executed asynchronously.
Journal ArticleDOI
The World’s Technological Capacity to Store, Communicate, and Compute Information
Martin Hilbert,Priscila López +1 more
TL;DR: An inventory of the world’s technological capacity from 1986 to 2007 reveals the evolution from analog to digital technologies, and the majority of the authors' technological memory has been in digital format since the early 2000s.
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Federated Optimization: Distributed Machine Learning for On-Device Intelligence
TL;DR: A new and increasingly relevant setting for distributed optimization in machine learning, where the data defining the optimization are unevenly distributed over an extremely large number of nodes, is introduced, to train a high-quality centralized model.
Journal ArticleDOI
Secure integration of IoT and Cloud Computing
TL;DR: A survey of IoT and Cloud Computing with a focus on the security issues of both technologies is presented, and it shows how the Cloud Computing technology improves the function of the IoT.