Performance analysis of compression algorithms for information security: A Review

Sharma, Neha; Batra, Usha

doi:10.4108/eai.13-7-2018.163503

Cited by 28 publications

(5 citation statements)

References 35 publications

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“…Compressing the transmission information can effectively reduce the amount of exchanged data per round given the total rounds unchanged. However, compression algorithms are computation resource-consuming [172]. When applying compression algorithms in FL, we have to balance computational resource and bandwidth usage.…”

Section: Improving the Scalability Of Fl Over Sdnmentioning

confidence: 99%

Applying Federated Learning in Software-Defined Networks: A Survey

Liao

Zhi

et al. 2022

Symmetry

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Federated learning (FL) is a type of distributed machine learning approacs that trains global models through the collaboration of participants. It protects data privacy as participants only contribute local models instead of sharing private local data. However, the performance of FL highly relies on the number of participants and their contributions. When applying FL over conventional computer networks, attracting more participants, encouraging participants to contribute more local resources, and enabling efficient and effective collaboration among participants become very challenging. As software-defined networks (SDNs) enable open and flexible networking architecture with separate control and data planes, SDNs provide standardized protocols and specifications to enable fine-grained collaborations among devices. Applying FL approaches over SDNs can take use such advantages to address challenges. A SDN control plane can have multiple controllers organized in layers; the controllers in the lower layer can be placed in the network edge to deal with the asymmetries in the attached switches and hosts, and the controller in the upper layer can supervise the whole network centrally and globally. Applying FL in SDNs with a layered-distributed control plane may be able to protect the data privacy of each participant while improving collaboration among participants to produce higher-quality models over asymmetric networks. Accordingly, this paper aims to make a comprehensive survey on the related mechanisms and solutions that enable FL in SDNs. It highlights three major challenges, an incentive mechanism, privacy and security, and model aggregation, which affect the quality and quantity of participants, the security and privacy in model transferring, and the performance of the global model, respectively. The state of the art in mechanisms and solutions that can be applied to address such challenges in the current literature are categorized based on the challenges they face, followed by suggestions of future research directions. To the best of our knowledge, this work is the first effort in surveying the state of the art in combining FL with SDNs.

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Section: Improving the Scalability Of Fl Over Sdnmentioning

confidence: 99%

Applying Federated Learning in Software-Defined Networks: A Survey

Liao

Zhi

et al. 2022

Symmetry

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“…At this point, we have no answers, and reading this book describing the current state of the art in the field of bio-mineralization will not provide many answers. We will approach the subject by discussing aspects of the bio-mineralization process in relation to the potential key effect mechanisms for different proxies [25]. Molecules can also operate on the morphology of the minerals This situation is especially evident in the silica-based case, which leads to the creation of synthetic materials with well-defined structures, sizes and shapes.…”

Section: Bio Mineralizationmentioning

confidence: 99%

An Overview of Dental Implant and Its Bio Mineralization Screening

Ramachandran¹,

Sivaji²,

Soniya³

2022

psr

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Dental and oral health is yours overall health and well-being are an essential part. Poor oral hygiene cavities and gums lead to diseases and are associated with heart disease, cancer and diabetes. Healthy teeth and maintaining the gums is a lifelong duty. Brushing, brushing and sugar intake if you quickly learn proper oral hygiene habits such as restraint, Expensive dental treatments and long-term health problems can be easily avoided. And may include the following: Keeping mouth and teeth clean to prevent. Dental care is the maintenance of healthy teeth; Oral hygiene is a dental problem in maintaining healthy teeth and gums, proper brushing, plaque removal, oral hygiene. A specialized tooth the office runs where you can brush your teeth for free and get a 20 percent discount on other dental related services. Tooth decay is caused by eating too many sweets and not brushing your teeth afterward. The dental hygienist assists the dentist in cleaning patients' teeth before doing any other work.

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“…Their newly designed healthcare model will assist medical professionals in accurately diagnosing the condition at an earlier stage. The primary goal of [33] is to ensure that their proposed work provides personal care for the patient in the real-time realm while spending as little time as possible doing so. In order to accomplish this, RF classifiers are educated and mapped using the information obtained from IoT sensors.…”

Section: Literature Reviewmentioning

confidence: 99%

Heart rate monitoring of physical fitness training load based on wavelet transform

Zhang¹,

Sharma

Samori

2022

The Journal of Engineering

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The detection of fluctuations is a crucial step in the process of isolating heart rate oscillation signals. This is necessary to circumvent the issue wherein a variety of auditory signals will invariably interfere with the collection of centre velocity signals during physical activity, which has a significant impact on the detection efficiency of automated testing. To obtain heart rate fluctuation signals that are more precise, the paper proposes a method of R‐wave identification that is based on heartbeat conversion while the athlete is exercising. The proposed model relies heavily on the technique of wave analysis to filter out the background noise of the heartbeat when the athlete exercises and isolates the R‐wave signal of the heartbeat from the background noise. The proposed algorithm is evaluated in terms of noise interference that removes approximately 90% of noise, the heart rate data which is obtained after the noise is normally distributed, the average detection rate of the R‐wave algorithm is 98.65%. It has been demonstrated that the detection accuracy of the proposed algorithm is sufficient to meet the requirements of generating heart rate oscillatory signals.

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Performance analysis of compression algorithms for information security: A Review

Cited by 28 publications

References 35 publications

Applying Federated Learning in Software-Defined Networks: A Survey

Applying Federated Learning in Software-Defined Networks: A Survey

An Overview of Dental Implant and Its Bio Mineralization Screening

Heart rate monitoring of physical fitness training load based on wavelet transform

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