955 research outputs found
One-Bit Byzantine-Tolerant Distributed Learning via Over-the-Air Computation
Distributed learning has become a promising computational parallelism
paradigm that enables a wide scope of intelligent applications from the
Internet of Things (IoT) to autonomous driving and the healthcare industry.
This paper studies distributed learning in wireless data center networks, which
contain a central edge server and multiple edge workers to collaboratively
train a shared global model and benefit from parallel computing. However, the
distributed nature causes the vulnerability of the learning process to faults
and adversarial attacks from Byzantine edge workers, as well as the severe
communication and computation overhead induced by the periodical information
exchange process. To achieve fast and reliable model aggregation in the
presence of Byzantine attacks, we develop a signed stochastic gradient descent
(SignSGD)-based Hierarchical Vote framework via over-the-air computation
(AirComp), where one voting process is performed locally at the wireless edge
by taking advantage of Bernoulli coding while the other is operated
over-the-air at the central edge server by utilizing the waveform superposition
property of the multiple-access channels. We comprehensively analyze the
proposed framework on the impacts including Byzantine attacks and the wireless
environment (channel fading and receiver noise), followed by characterizing the
convergence behavior under non-convex settings. Simulation results validate our
theoretical achievements and demonstrate the robustness of our proposed
framework in the presence of Byzantine attacks and receiver noise.Comment: This work has been submitted to the IEEE for possible publication.
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Hierarchical video surveillance architecture: a chassis for video big data analytics and exploration
There is increasing reliance on video surveillance systems for systematic derivation, analysis and interpretation of the data needed for predicting, planning, evaluating and implementing public safety. This is evident from the massive number of surveillance cameras deployed across public locations. For example, in July 2013, the British Security Industry Association (BSIA) reported that over 4 million CCTV cameras had been installed in Britain alone. The BSIA also reveal that only 1.5% of these are state owned. In this paper, we propose a framework that allows access to data from privately owned cameras, with the aim of increasing the efficiency and accuracy of public safety planning, security activities, and decision support systems that are based on video integrated surveillance systems. The accuracy of results obtained from government-owned public safety infrastructure would improve greatly if privately owned surveillance systems ‘expose’ relevant video-generated metadata events, such as triggered alerts and also permit query of a metadata repository. Subsequently, a police officer, for example, with an appropriate level of system permission can query unified video systems across a large geographical area such as a city or a country to predict the location of an interesting entity, such as a pedestrian or a vehicle. This becomes possible with our proposed novel hierarchical architecture, the Fused Video Surveillance Architecture (FVSA). At the high level, FVSA comprises of a hardware framework that is supported by a multi-layer abstraction software interface. It presents video surveillance systems as an adapted computational grid of intelligent services, which is integration-enabled to communicate with other compatible systems in the Internet of Things (IoT)
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