IEEE INFOCOM 2023 - IEEE Conference on Computer Communications Workshops (INFOCOM WKSHPS)最新文献

A Novel Hierarchically Decentralized Federated Learning Framework in 6G Wireless Networks 6G无线网络中一种新的分层分散联邦学习框架

IEEE INFOCOM 2023 - IEEE Conference on Computer Communications Workshops (INFOCOM WKSHPS) Pub Date : 2023-05-20 DOI: 10.1109/INFOCOMWKSHPS57453.2023.10226164

J. Zhang, Li Chen, Xiaohui Chen, Guo Wei

引用次数: 0

Federated Learning at the Edge: An Interplay of Mini-batch Size and Aggregation Frequency 边缘的联邦学习:小批大小和聚合频率的相互作用

IEEE INFOCOM 2023 - IEEE Conference on Computer Communications Workshops (INFOCOM WKSHPS) Pub Date : 2023-05-20 DOI: 10.1109/INFOCOMWKSHPS57453.2023.10226109

Weijie Liu, Xiaoxi Zhang, Jingpu Duan, Carlee Joe-Wong, Zhi Zhou, Xu Chen

{"title":"Federated Learning at the Edge: An Interplay of Mini-batch Size and Aggregation Frequency","authors":"Weijie Liu, Xiaoxi Zhang, Jingpu Duan, Carlee Joe-Wong, Zhi Zhou, Xu Chen","doi":"10.1109/INFOCOMWKSHPS57453.2023.10226109","DOIUrl":"https://doi.org/10.1109/INFOCOMWKSHPS57453.2023.10226109","url":null,"abstract":"Federated Learning (FL) is a distributed learning paradigm that can coordinate heterogeneous edge devices to perform model training without sharing private raw data. Prior works on the convergence analysis of FL have focused on mini-batch size and aggregation frequency separately. However, increasing the batch size and the number of local updates can differently affect model performance and system overhead. This paper proposes a novel model in quantifying the interplay of FL mini-batch size and aggregation frequency to navigate the unique trade-offs among convergence, completion time, and resource cost. We obtain a new convergence bound for synchronous FL with respect to these decision variables under heterogeneous training datasets at different devices. Based on this bound, we derive closed-form solutions for co-optimized mini-batch size and aggregation frequency, uniformly among devices. We then design an efficient exact algorithm to optimize heterogeneous mini-batch configurations, further improving the model accuracy. An adaptive control algorithm is also proposed to dynamically adjust the batch sizes and the number of local updates per round. Extensive experiments demonstrate the superiority of our offline optimized solutions and online adaptive algorithm.","PeriodicalId":354290,"journal":{"name":"IEEE INFOCOM 2023 - IEEE Conference on Computer Communications Workshops (INFOCOM WKSHPS)","volume":"26 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-05-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124904180","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

Accurate Anomaly Interval Recognition and Fault Classification by Pattern Mining and Clustering 基于模式挖掘和聚类的准确异常区间识别与故障分类

IEEE INFOCOM 2023 - IEEE Conference on Computer Communications Workshops (INFOCOM WKSHPS) Pub Date : 2023-05-20 DOI: 10.1109/INFOCOMWKSHPS57453.2023.10226079

Ningyuan Sun, Hongyun Zheng, Yishuai Chen, Yajun Liu, Jinuo Fang

{"title":"Accurate Anomaly Interval Recognition and Fault Classification by Pattern Mining and Clustering","authors":"Ningyuan Sun, Hongyun Zheng, Yishuai Chen, Yajun Liu, Jinuo Fang","doi":"10.1109/INFOCOMWKSHPS57453.2023.10226079","DOIUrl":"https://doi.org/10.1109/INFOCOMWKSHPS57453.2023.10226079","url":null,"abstract":"To maintain the stability and reliability of a large-scale information system, monitoring its Key Performance Indicators (KPIs) time series and detecting their anomalies are very important. In practice, however, multivariate time series anomaly detection is challenging due to the large dimension of time series, diverse anomalous patterns, and their complex relationships. In addition, KPIs may exhibit different patterns when different types of faults occur, which aggravates the difficulty of anomaly detection. In this paper, we propose an accurate KPI anomaly detection and fault classification method, which can adapt to multiple metrics and diverse fault types. It can automatically extract common anomalous patterns from different KPI responses when faults occur and accurately determine the fault intervals. In this method, we do not need to deploy a lot of different anomaly detectors, and can conduct both anomaly detection and fault classification simultaneously. Experimental results on the real-world Exathlon benchmark dataset show that our algorithm can accurately recognize the anomaly intervals and classify the faults, with F1-score 0.94.","PeriodicalId":354290,"journal":{"name":"IEEE INFOCOM 2023 - IEEE Conference on Computer Communications Workshops (INFOCOM WKSHPS)","volume":"39 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-05-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126085328","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

Pragmatic Communication: Bridging Neural Networks for Distributed Agents 语用沟通:桥接分布式代理的神经网络

IEEE INFOCOM 2023 - IEEE Conference on Computer Communications Workshops (INFOCOM WKSHPS) Pub Date : 2023-05-20 DOI: 10.1109/INFOCOMWKSHPS57453.2023.10226074

Tianhao Guo

引用次数: 0

FPGA-based Deterministic and Low-Latency Control for Distributed Quantum Computing 基于fpga的分布式量子计算确定性和低延迟控制

IEEE INFOCOM 2023 - IEEE Conference on Computer Communications Workshops (INFOCOM WKSHPS) Pub Date : 2023-05-20 DOI: 10.1109/INFOCOMWKSHPS57453.2023.10226129

R. Oliveira, S. Bahrani, E. Arabul, Rui Wang, R. Nejabati, D. Simeonidou

{"title":"FPGA-based Deterministic and Low-Latency Control for Distributed Quantum Computing","authors":"R. Oliveira, S. Bahrani, E. Arabul, Rui Wang, R. Nejabati, D. Simeonidou","doi":"10.1109/INFOCOMWKSHPS57453.2023.10226129","DOIUrl":"https://doi.org/10.1109/INFOCOMWKSHPS57453.2023.10226129","url":null,"abstract":"Distributed quantum computing is a promising solution for creating large-scale quantum computers. In such scenarios, quantum processing units (QPUs) are connected to each other via quantum and classical links. To increase the performance in such a distributed manner, and due to the fragile nature of quantum bits and their decoherence with time, the impact of classical links such as communication latency and jitter between QPUs shall be considered. Here, we propose a low-latency and time-deterministic FPGA-based network supporting execution of distributed quantum circuits. We focus on transmissions of measurement result and control messages as well as synchronization in a distributed network. We demonstrate that a message is transmitted with 361.60 ns between QPUs using optical Ethernet. Synchronization reaches 9.6 ns precision using only Ethernet frames and can reach 21 ps with an external clock. Further, a use-case example of an Inverse Quantum Fourier Transform is implemented to evaluate the impact in terms of latency for inter-QPU data transfers. Our theoretical error analysis and simulation results show that the latency added by our FPGA-controlled network has a negligible impact on the quantum algorithm performance for practical values of memory decoherence time.","PeriodicalId":354290,"journal":{"name":"IEEE INFOCOM 2023 - IEEE Conference on Computer Communications Workshops (INFOCOM WKSHPS)","volume":"108 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-05-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122527995","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

CICADA: Cloud-based Intelligent Classification and Active Defense Approach for IoT Security CICADA:基于云的物联网安全智能分类与主动防御方法

IEEE INFOCOM 2023 - IEEE Conference on Computer Communications Workshops (INFOCOM WKSHPS) Pub Date : 2023-05-20 DOI: 10.1109/INFOCOMWKSHPS57453.2023.10225954

R. Neupane, Trevor Zobrist, K. Neupane, Shaynoah Bedford, Shreyas Prabhudev, Trevontae Haughton, Jianli Pan, P. Calyam

{"title":"CICADA: Cloud-based Intelligent Classification and Active Defense Approach for IoT Security","authors":"R. Neupane, Trevor Zobrist, K. Neupane, Shaynoah Bedford, Shreyas Prabhudev, Trevontae Haughton, Jianli Pan, P. Calyam","doi":"10.1109/INFOCOMWKSHPS57453.2023.10225954","DOIUrl":"https://doi.org/10.1109/INFOCOMWKSHPS57453.2023.10225954","url":null,"abstract":"Internet of Things (IoT) devices capture and process sensitive personally identifiable information such as e.g., camera feeds/health data from enterprises and households. These devices are becoming targets of prominent attacks such as Distributed-Denial-of-Service (DDoS) and Botnets, as well as sophisticated attacks (e.g., Zero Click) that are elusive by design. There is a need for cyber deception techniques that can automate attack impact mitigation at the scale that IoT networks demand. In this paper, we present a novel cloud-based active defense approach viz., “CICADA”, to detect and counter attacks that target vulnerable IoT networks. Specifically, we propose a multi-model detection engine featuring a pipeline of machine/deep learning classifiers to label inbound packet flows. In addition, we devise an edge-based defense engine that utilizes three simulated deception environments (Honeynet, Pseudocomb, and Honeyclone) with increasing pretense capabilities to deceive the attacker and lower the attack risk. Our deception environments are based on a CFO triad (cost, fidelity, observability) for designing system architectures to handle attacks with diverse detection characteristics. We evaluate the effectiveness of these architectures on an enterprise IoT network setting with a scale of thousands of devices. Our detection results show ≃73% accuracy for the low observability attack (Zero Click) corresponding to the BleedingTooth exploit that allows for unauthenticated remote attacks on vulnerable devices. Furthermore, we evaluate the different deception environments based on their risk mitigation potential and associated costs. Our simulation results show that the Honeyclone is able to reduce risk by ≃88% when compared to a network without any defenses.","PeriodicalId":354290,"journal":{"name":"IEEE INFOCOM 2023 - IEEE Conference on Computer Communications Workshops (INFOCOM WKSHPS)","volume":"193 ","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-05-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114058634","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

Poster: Digital Network Twin via Learning-Based Simulator 海报:基于学习模拟器的数字网络双胞胎

IEEE INFOCOM 2023 - IEEE Conference on Computer Communications Workshops (INFOCOM WKSHPS) Pub Date : 2023-05-20 DOI: 10.1109/INFOCOMWKSHPS57453.2023.10226169

Yuru Zhang, Yongjie Xue, Qiang Liu, Nakjung Choi

引用次数: 0

Wireless-Sec 2023 Program 无线- sec 2023计划

IEEE INFOCOM 2023 - IEEE Conference on Computer Communications Workshops (INFOCOM WKSHPS) Pub Date : 2023-05-20 DOI: 10.1109/infocomwkshps57453.2023.10226002

引用次数: 0

Coding-Aware Rate Splitting for Distributed Coded Edge Learning 分布式编码边缘学习的编码感知速率分割

IEEE INFOCOM 2023 - IEEE Conference on Computer Communications Workshops (INFOCOM WKSHPS) Pub Date : 2023-05-20 DOI: 10.1109/INFOCOMWKSHPS57453.2023.10226011

Tianheng Li, Jingzhe Zhang, Xiaofan He

{"title":"Coding-Aware Rate Splitting for Distributed Coded Edge Learning","authors":"Tianheng Li, Jingzhe Zhang, Xiaofan He","doi":"10.1109/INFOCOMWKSHPS57453.2023.10226011","DOIUrl":"https://doi.org/10.1109/INFOCOMWKSHPS57453.2023.10226011","url":null,"abstract":"Driven by the explosive escalation of machine learning applications, considerable efforts have been devoted to distributed edge learning. To alleviate the so-called straggling issue, coded computing that injects elaborate redundancy into computation emerges as a promising solution, which in turn ignites the recent research interests in distributed coded edge learning. Albeit effectively mitigating straggling, coded edge learning brings new challenges in communications. In particular, existing transmission schemes are mainly designed for conventional distributed edge learning, where the data offloaded to different edge nodes (ENs) are non-overlapping. They cannot achieve the best performance when applied directly to distributed coded edge learning, due to the redundancy among the data for different ENs in the coded settings. To the best of our knowledge, a tailor-designed transmission scheme for distributed coded edge learning still remains open. With this consideration, a novel coding-aware rate splitting scheme is proposed in this work, which splits the data to different ENs in a coding-aware way to avoid transmission redundancy and enables multiple simultaneous multi-casts to the ENs. To minimize the overall processing latency, an iterative optimization algorithm is developed based on the concave-convex procedure (CCCP) framework. Simulations demonstrate that the proposed scheme can substantially reduce the overall latency of distributed coded edge learning as compared to the baselines.","PeriodicalId":354290,"journal":{"name":"IEEE INFOCOM 2023 - IEEE Conference on Computer Communications Workshops (INFOCOM WKSHPS)","volume":"65 4 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-05-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129600205","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

CVCA: A Complex-Valued Classifiable Autoencoder for MmWave Massive MIMO Physical Layer Authentication CVCA:用于毫米波大规模MIMO物理层认证的复值可分类自编码器

IEEE INFOCOM 2023 - IEEE Conference on Computer Communications Workshops (INFOCOM WKSHPS) Pub Date : 2023-05-20 DOI: 10.1109/INFOCOMWKSHPS57453.2023.10225831

Xinyuan Zeng, Chao Wang, Cheng-Cai Wang, Zan Li

{"title":"CVCA: A Complex-Valued Classifiable Autoencoder for MmWave Massive MIMO Physical Layer Authentication","authors":"Xinyuan Zeng, Chao Wang, Cheng-Cai Wang, Zan Li","doi":"10.1109/INFOCOMWKSHPS57453.2023.10225831","DOIUrl":"https://doi.org/10.1109/INFOCOMWKSHPS57453.2023.10225831","url":null,"abstract":"For protecting millimeter wave (mmWave) communications from clone attacks, this paper employs the deep learning to propose a physical layer authentication (PLA) approach for detecting attackers and classifying multiple legitimate nodes simultaneously. Different from conventional upper-layer authentication mechanisms, the proposed PLA approach exploits the spatial and temporal characteristics of mmWave channels to extract the unique fingerprints for building a lightweight channel-based authentication method. However, the existing threshold-based PLA methods could not discriminate multiple nodes, and supervised learning based approaches have limited application due to the unavailability of attackers' channel state information (CSI) in practice. Besides, traditional real-valued deep neural networks cannot exploit the phase information of complex channels efficiently, which is unsuitable for designing the PLA scheme. Considering these, we propose a complex-valued classifiable autoencoder induced PLA scheme that includes a novel complex-valued long short-term memory (LSTM) module. Simulation results validate the superiority of our proposed PLA approach by comparing it with existing approaches and demonstrate that the detection probability of clone attacks positively correlates with antenna number. The classification performance is satisfactory even under the challenging experimental condition.","PeriodicalId":354290,"journal":{"name":"IEEE INFOCOM 2023 - IEEE Conference on Computer Communications Workshops (INFOCOM WKSHPS)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-05-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129694045","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