2024 16th International Conference on COMmunication Systems & NETworkS (COMSNETS)最新文献_第4页

Towards an Efficient Federated Learning Framework with Selective Aggregation 建立具有选择性聚合功能的高效联盟学习框架

2024 16th International Conference on COMmunication Systems & NETworkS (COMSNETS) Pub Date : 2024-01-03 DOI: 10.1109/COMSNETS59351.2024.10426966

Anirudha Kulkarni, Abhinav Kumar, R. Shorey, Rohit Verma

{"title":"Towards an Efficient Federated Learning Framework with Selective Aggregation","authors":"Anirudha Kulkarni, Abhinav Kumar, R. Shorey, Rohit Verma","doi":"10.1109/COMSNETS59351.2024.10426966","DOIUrl":"https://doi.org/10.1109/COMSNETS59351.2024.10426966","url":null,"abstract":"Federated Learning shows promise for collaborative, decentralized machine learning but faces efficiency challenges, primarily network straggler-induced latency bottlenecks and the need for complex aggregation techniques. To address these issues, ongoing research explores asynchronous FL, i.e., federated learning models, including an Asynchronous Parallel Federated Learning [5] framework. This study investigates the impact of varying worker node numbers on key metrics. More nodes offer faster convergence but may increase communication overhead and straggler vulnerability. We aim to quantify how the number of worker node variations for one global aggregation can affect convergence speed, communication efficiency, model accuracy, and system robustness, optimizing asynchronous FL system configurations. This work is crucial for practical and scalable FL applications, mitigating network stragglers, data distribution, and security challenges. This work analyses Asynchronous Parallel Federated Learning and showcases a paradigm shift in the approach by selectively aggregating early arriving worker node updates with a novel parameter ‘x’, improving efficiency and reshaping FL.","PeriodicalId":518748,"journal":{"name":"2024 16th International Conference on COMmunication Systems & NETworkS (COMSNETS)","volume":"241 1-2","pages":"623-627"},"PeriodicalIF":0.0,"publicationDate":"2024-01-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140532587","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Distributed Compressive Spectrum Sensing Using Robust Power Estimation Techniques Under Non-Gaussian Noise 利用非高斯噪声下的鲁棒功率估计技术进行分布式压缩频谱传感

2024 16th International Conference on COMmunication Systems & NETworkS (COMSNETS) Pub Date : 2024-01-03 DOI: 10.1109/COMSNETS59351.2024.10427408

B. Bhavana, S. Sabat, N. Swetha, Trilochan Panigrahi

引用次数: 0

Improved Child Safety Using Edge-Fog-Cloud Enabled Smart IoT Wearable Device: An Architecture 利用边缘-雾-云智能物联网可穿戴设备提高儿童安全：架构

2024 16th International Conference on COMmunication Systems & NETworkS (COMSNETS) Pub Date : 2024-01-03 DOI: 10.1109/COMSNETS59351.2024.10427466

Neha Agrawal, Rohit Kumar, S. Tapaswi

{"title":"Improved Child Safety Using Edge-Fog-Cloud Enabled Smart IoT Wearable Device: An Architecture","authors":"Neha Agrawal, Rohit Kumar, S. Tapaswi","doi":"10.1109/COMSNETS59351.2024.10427466","DOIUrl":"https://doi.org/10.1109/COMSNETS59351.2024.10427466","url":null,"abstract":"Child safety is of paramount importance as it directly impacts the development, well-being, and future of the children. Ensuring their safety is a collective responsibility shared by parents, caregivers, communities, and society as a whole. Smart wearable devices for child safety can play a significant role in enhancing the security and well-being of children. In market, several child tracking and monitoring devices are available but have some limitations such as unreliable communication, low accuracy, unavailability of real-time data processing and analysis, etc. To address these limitations, an Edge-Fog-Cloud enabled smart IoT wearable device for improved child safety is proposed in this paper. The architecture of the proposed system is composed of two modules, namely child module and parent module. The child module consists of four submodules for GPS, hearbeat, voice and image sensing. The parent module has a smart-phone application submodule. Further, the methodology and process workflow of the proposed architecture are explained in this paper. The authors are working on the implementation of the proposed architecture and it is the future work of this paper.","PeriodicalId":518748,"journal":{"name":"2024 16th International Conference on COMmunication Systems & NETworkS (COMSNETS)","volume":"314 8","pages":"61-66"},"PeriodicalIF":0.0,"publicationDate":"2024-01-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140532992","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Unlocking Telecom Domain Knowledge Using LLMs 利用 LLM 解锁电信领域知识

2024 16th International Conference on COMmunication Systems & NETworkS (COMSNETS) Pub Date : 2024-01-03 DOI: 10.1109/COMSNETS59351.2024.10427044

Sujoy Roychowdhury, Nishkarsh Jain, Sumit Soman

引用次数: 0

Mitigating Bias in Machine Learning Models for Phishing Webpage Detection 减少用于检测网络钓鱼网页的机器学习模型中的偏差

2024 16th International Conference on COMmunication Systems & NETworkS (COMSNETS) Pub Date : 2024-01-03 DOI: 10.1109/COMSNETS59351.2024.10427170

Aditya Kulkarni, Vivek Balachandran, D. Divakaran, Tamal Das

{"title":"Mitigating Bias in Machine Learning Models for Phishing Webpage Detection","authors":"Aditya Kulkarni, Vivek Balachandran, D. Divakaran, Tamal Das","doi":"10.1109/COMSNETS59351.2024.10427170","DOIUrl":"https://doi.org/10.1109/COMSNETS59351.2024.10427170","url":null,"abstract":"The widespread accessibility of the Internet has led to a surge in online fraudulent activities, underscoring the necessity of shielding users' sensitive information from cybercriminals. Phishing, a well-known cyberattack, revolves around the creation of phishing webpages and the dissemination of corresponding URLs, aiming to deceive users into sharing their sensitive information, often for identity theft or financial gain. Various techniques are available for preemptively categorizing zero-day phishing URLs by distilling unique attributes and constructing predictive models. However, these existing techniques encounter unresolved issues. This proposal delves into persistent challenges within phishing detection solutions, particularly concentrated on the preliminary phase of assembling comprehensive datasets, and proposes a potential solution in the form of a tool engineered to alleviate bias in ML models. Such a tool can generate phishing webpages for any given set of legitimate URLs, infusing randomly selected content and visual-based phishing features. Furthermore, we contend that the tool holds the potential to assess the efficacy of existing phishing detection solutions, especially those trained on confined datasets.","PeriodicalId":518748,"journal":{"name":"2024 16th International Conference on COMmunication Systems & NETworkS (COMSNETS)","volume":"290 1","pages":"430-432"},"PeriodicalIF":0.0,"publicationDate":"2024-01-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140532825","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

CloudAdvisor for Sustainable and Data Residency Compliant Data Placement in Multi-Cloud CloudAdvisor：在多云中实现可持续且符合数据驻留标准的数据放置

2024 16th International Conference on COMmunication Systems & NETworkS (COMSNETS) Pub Date : 2024-01-03 DOI: 10.1109/COMSNETS59351.2024.10427201

Pankaj Sahu, Shubhro Roy, M. Gharote

引用次数: 0

Divide-and-Conquer for Internet-of-Things: Discrete vs Continuous Groups 物联网的分而治之：离散群组与连续群组

2024 16th International Conference on COMmunication Systems & NETworkS (COMSNETS) Pub Date : 2024-01-03 DOI: 10.1109/COMSNETS59351.2024.10427391

Jagnyashini Debadarshini, Sudip Saha

引用次数: 0

Multi-AP Coordination with Centralized Overlay Time-Sliced Scheduling in WiFi Network WiFi 网络中的多 AP 协调与集中叠加分时调度

2024 16th International Conference on COMmunication Systems & NETworkS (COMSNETS) Pub Date : 2024-01-03 DOI: 10.1109/COMSNETS59351.2024.10427467

Shivam Vinayak Vatsa, Anusha Gp, Rishabh Roy, S.V.R. Anand, Anurag Kumar, Joy Kuri

引用次数: 0

iMat:A Non-Intrusive and Low Cost Sleep Posture Estimation Mat iMat：非侵入式低成本睡姿估测垫

2024 16th International Conference on COMmunication Systems & NETworkS (COMSNETS) Pub Date : 2024-01-03 DOI: 10.1109/COMSNETS59351.2024.10426979

Ramandeep Singh, Nishtha Phutela, Dheryta Jaisinghani

引用次数: 0

Occupancy Prediction at Transit Stops Using ANN 使用 ANN 预测公交站点的占用率

2024 16th International Conference on COMmunication Systems & NETworkS (COMSNETS) Pub Date : 2024-01-03 DOI: 10.1109/COMSNETS59351.2024.10427191

Jatin Bhandari, Rupam R. Fedujwar, Amit Agarwal

{"title":"Occupancy Prediction at Transit Stops Using ANN","authors":"Jatin Bhandari, Rupam R. Fedujwar, Amit Agarwal","doi":"10.1109/COMSNETS59351.2024.10427191","DOIUrl":"https://doi.org/10.1109/COMSNETS59351.2024.10427191","url":null,"abstract":"Crowding in public transport is a known problem that negatively affects passengers' travel experience as well as degrades the performance of public transport. It may occur at the station entrance, at the stop, in-vehicle, and platforms. The present study focuses on stop-level occupancy prediction, which can be further used for crowding estimation. At first, boarding stops are inferred using timestamps of the electronic ticketing machine (ETM) data and validated using automated vehicle location (AVL) data. Further, alighting stops are inferred using a combination of explanatory variables, such as point of interest data, population density, green area, industrial area, and residential areas. The t-distributed Stochastic Neighbourhood Embedding algorithm is used to reduce the dimensionality for alighting inferences. The stop occupancy is then predicted using an artificial neural network (ANN) model. A case study of Delhi, India, is presented, and four months of ticketing data are used to demonstrate the performance of the proposed prediction model. A network-level analysis is performed to identify the critical stons.","PeriodicalId":518748,"journal":{"name":"2024 16th International Conference on COMmunication Systems & NETworkS (COMSNETS)","volume":"98 1","pages":"825-832"},"PeriodicalIF":0.0,"publicationDate":"2024-01-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140532740","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0