87,290 research outputs found
A Framework for Uncertain Cloud Data Security and Recovery Based on Hybrid Multi-User Medical Decision Learning Patterns
Machine learning has been supporting real-time cloud based medical computing systems. However, most of the computing servers are independent of data security and recovery scheme in multiple virtual machines due to high computing cost and time. Also, this cloud based medical applications require static security parameters for cloud data security. Cloud based medical applications require multiple servers to store medical records or machine learning patterns for decision making. Due to high Uncertain computational memory and time, these cloud systems require an efficient data security framework to provide strong data access control among the multiple users. In this work, a hybrid cloud data security framework is developed to improve the data security on the large machine learning patterns in real-time cloud computing environment. This work is implemented in two phases’ i.e. data replication phase and multi-user data access security phase. Initially, machine decision patterns are replicated among the multiple servers for Uncertain data recovering phase. In the multi-access cloud data security framework, a hybrid multi-access key based data encryption and decryption model is implemented on the large machine learning medical patterns for data recovery and security process. Experimental results proved that the present two-phase data recovering, and security framework has better computational efficiency than the conventional approaches on large medical decision patterns
Safe, Secure Executions at the Network Edge : Coordinating Cloud, Edge, and Fog Computing
System design where cyber-physical applications are securely coordinated from the cloud may simplify the development process. However, all private data are then pushed to these remote “swamps,” and human users lose actual control as compared to when the applications are executed directly on their devices. At the same time, computing at the network edge is still lacking support for such straightforward multidevice development, which is essential for a wide range of dynamic cyber-physical services. This article proposes a novel programming model as well as contributes the associated secure-connectivity framework for leveraging safe coordinated device proximity as an additional degree of freedom between the remote cloud and the safety-critical network edge, especially under uncertain environment constraints. This article is part of a special issue on Software Safety and Security Risk Mitigation in Cyber-physical Systems.Peer reviewe
GitFL: Adaptive Asynchronous Federated Learning using Version Control
As a promising distributed machine learning paradigm that enables
collaborative training without compromising data privacy, Federated Learning
(FL) has been increasingly used in AIoT (Artificial Intelligence of Things)
design. However, due to the lack of efficient management of straggling devices,
existing FL methods greatly suffer from the problems of low inference accuracy
and long training time. Things become even worse when taking various uncertain
factors (e.g., network delays, performance variances caused by process
variation) existing in AIoT scenarios into account. To address this issue, this
paper proposes a novel asynchronous FL framework named GitFL, whose
implementation is inspired by the famous version control system Git. Unlike
traditional FL, the cloud server of GitFL maintains a master model (i.e., the
global model) together with a set of branch models indicating the trained local
models committed by selected devices, where the master model is updated based
on both all the pushed branch models and their version information, and only
the branch models after the pull operation are dispatched to devices. By using
our proposed Reinforcement Learning (RL)-based device selection mechanism, a
pulled branch model with an older version will be more likely to be dispatched
to a faster and less frequently selected device for the next round of local
training. In this way, GitFL enables both effective control of model staleness
and adaptive load balance of versioned models among straggling devices, thus
avoiding the performance deterioration. Comprehensive experimental results on
well-known models and datasets show that, compared with state-of-the-art
asynchronous FL methods, GitFL can achieve up to 2.64X training acceleration
and 7.88% inference accuracy improvements in various uncertain scenarios
Earth System Modeling 2.0: A Blueprint for Models That Learn From Observations and Targeted High-Resolution Simulations
Climate projections continue to be marred by large uncertainties, which
originate in processes that need to be parameterized, such as clouds,
convection, and ecosystems. But rapid progress is now within reach. New
computational tools and methods from data assimilation and machine learning
make it possible to integrate global observations and local high-resolution
simulations in an Earth system model (ESM) that systematically learns from
both. Here we propose a blueprint for such an ESM. We outline how
parameterization schemes can learn from global observations and targeted
high-resolution simulations, for example, of clouds and convection, through
matching low-order statistics between ESMs, observations, and high-resolution
simulations. We illustrate learning algorithms for ESMs with a simple dynamical
system that shares characteristics of the climate system; and we discuss the
opportunities the proposed framework presents and the challenges that remain to
realize it.Comment: 32 pages, 3 figure
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