Pub Date : 2024-02-05DOI: 10.1109/TSUSC.2024.3362074
Ke Gu;XingQiang Wang;Xiong Li
The security of fog computing has been researched and concerned with its development, where malicious attacks pose a greater threat to distributed data storage based on fog computing. Also, the rapid increasing on the number of terminal devices has raised the importance of fog computing-based distributed data storage. In response to this demand, it is essential to establish a secure and privacy-preserving distributed data auditing method that enables security protection of stored data and effective control over identities of auditors. In this paper, we propose a dynamic outsourced data audit scheme for Merkle hash grid-based fog storage with privacy-preserving, where fog servers are used to undertake partial outsourced computation and data storage. Our scheme can provide the function of privacy-preserving for outsourced data by blinding original stored data, and supports data owners to define their auditing access policies by the linear secret-sharing scheme to control the identities of auditors. Further, the construction of Merkle hash grid is used to improve the efficiency of dynamic data operations. Also, a server locating approach is proposed to enable the third-part auditor to identify specific malicious data fog servers within distributed data storage. Under the proposed security model, the security of our scheme can be proved, which can further provide collusion resistance and privacy-preserving for outsourced data. Additionally, both theoretical and experimental evaluations illustrate the efficiency of our proposed scheme.
{"title":"Dynamic Outsourced Data Audit Scheme for Merkle Hash Grid-Based Fog Storage With Privacy-Preserving","authors":"Ke Gu;XingQiang Wang;Xiong Li","doi":"10.1109/TSUSC.2024.3362074","DOIUrl":"https://doi.org/10.1109/TSUSC.2024.3362074","url":null,"abstract":"The security of fog computing has been researched and concerned with its development, where malicious attacks pose a greater threat to distributed data storage based on fog computing. Also, the rapid increasing on the number of terminal devices has raised the importance of fog computing-based distributed data storage. In response to this demand, it is essential to establish a secure and privacy-preserving distributed data auditing method that enables security protection of stored data and effective control over identities of auditors. In this paper, we propose a dynamic outsourced data audit scheme for Merkle hash grid-based fog storage with privacy-preserving, where fog servers are used to undertake partial outsourced computation and data storage. Our scheme can provide the function of privacy-preserving for outsourced data by blinding original stored data, and supports data owners to define their auditing access policies by the linear secret-sharing scheme to control the identities of auditors. Further, the construction of Merkle hash grid is used to improve the efficiency of dynamic data operations. Also, a server locating approach is proposed to enable the third-part auditor to identify specific malicious data fog servers within distributed data storage. Under the proposed security model, the security of our scheme can be proved, which can further provide collusion resistance and privacy-preserving for outsourced data. Additionally, both theoretical and experimental evaluations illustrate the efficiency of our proposed scheme.","PeriodicalId":13268,"journal":{"name":"IEEE Transactions on Sustainable Computing","volume":null,"pages":null},"PeriodicalIF":3.0,"publicationDate":"2024-02-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141965829","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-02-01DOI: 10.1109/TSUSC.2024.3360975
Fu Jiang;Yaoxin Xia;Lisen Yan;Weirong Liu;Xiaoyong Zhang;Heng Li;Jun Peng
Battery degradation is a main hinder to extend the persistent lifespan of the portable heterogeneous computing device. Excessive energy consumption and prominent current fluctuations can lead to a sharp decline of battery endurance. To address this issue, a battery-aware workflow scheduling algorithm is proposed to maximize the battery lifetime and release the computing potential of the device fully. First, a dynamic optimal budget strategy is developed to select the highest cost-effectiveness processors to meet the deadline of each task, accelerating the budget optimization by incorporating deep neural network. Second, an integer-programming greedy strategy is utilized to determine the start time of each task, minimizing the fluctuation of the battery supply current to mitigate the battery degradation. Finally, a long-term operation experiment and Monte Carlo experiments are performed on the battery simulator, SLIDE. The experimental results under real operating conditions for more than 1800 hours validate that the proposed scheduling algorithm can effectively extend the battery life by 7.31%-8.23%. The results on various parallel workflows illustrate that the proposed algorithm has comparable performance with speed improvement over the integer programming method.
{"title":"Battery-Aware Workflow Scheduling for Portable Heterogeneous Computing","authors":"Fu Jiang;Yaoxin Xia;Lisen Yan;Weirong Liu;Xiaoyong Zhang;Heng Li;Jun Peng","doi":"10.1109/TSUSC.2024.3360975","DOIUrl":"https://doi.org/10.1109/TSUSC.2024.3360975","url":null,"abstract":"Battery degradation is a main hinder to extend the persistent lifespan of the portable heterogeneous computing device. Excessive energy consumption and prominent current fluctuations can lead to a sharp decline of battery endurance. To address this issue, a battery-aware workflow scheduling algorithm is proposed to maximize the battery lifetime and release the computing potential of the device fully. First, a dynamic optimal budget strategy is developed to select the highest cost-effectiveness processors to meet the deadline of each task, accelerating the budget optimization by incorporating deep neural network. Second, an integer-programming greedy strategy is utilized to determine the start time of each task, minimizing the fluctuation of the battery supply current to mitigate the battery degradation. Finally, a long-term operation experiment and Monte Carlo experiments are performed on the battery simulator, SLIDE. The experimental results under real operating conditions for more than 1800 hours validate that the proposed scheduling algorithm can effectively extend the battery life by 7.31%-8.23%. The results on various parallel workflows illustrate that the proposed algorithm has comparable performance with speed improvement over the integer programming method.","PeriodicalId":13268,"journal":{"name":"IEEE Transactions on Sustainable Computing","volume":null,"pages":null},"PeriodicalIF":3.0,"publicationDate":"2024-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141965765","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Computing servers have played a key role in developing and processing emerging compute-intensive applications in recent years. Consolidating multiple virtual machines (VMs) inside one server to run various applications introduces severe competence for limited resources among VMs. Many techniques such as VM scheduling and resource provisioning are proposed to maximize the cost-efficiency of the computing servers while alleviating the performance inference between VMs. However, these management techniques require accurate performance prediction of the application running inside the VM, which is challenging to get in the public cloud due to the black-box nature of the VMs. From this perspective, this paper proposes a novel machine learning-based performance prediction approach for applications running in the cloud. To achieve high-accuracy predictions for black-box VMs, the proposed method first identifies the running application inside the virtual machine. It then selects highly correlated runtime metrics as the input of the machine learning approach to accurately predict the performance level of the cloud application. Experimental results with state-of-the-art cloud benchmarks demonstrate that our proposed method outperforms existing prediction methods by more than 2× in terms of the worst prediction error. In addition, we successfully tackle the challenge of performance prediction for applications with variable workloads by introducing the performance degradation index, which other comparison methods fail to consider. The workflow versatility of the proposed approach has been verified with different modern servers and VM configurations.
{"title":"CloudProphet: A Machine Learning-Based Performance Prediction for Public Clouds","authors":"Darong Huang;Luis Costero;Ali Pahlevan;Marina Zapater;David Atienza","doi":"10.1109/TSUSC.2024.3359325","DOIUrl":"https://doi.org/10.1109/TSUSC.2024.3359325","url":null,"abstract":"Computing servers have played a key role in developing and processing emerging compute-intensive applications in recent years. Consolidating multiple virtual machines (VMs) inside one server to run various applications introduces severe competence for limited resources among VMs. Many techniques such as VM scheduling and resource provisioning are proposed to maximize the cost-efficiency of the computing servers while alleviating the performance inference between VMs. However, these management techniques require accurate performance prediction of the application running inside the VM, which is challenging to get in the public cloud due to the black-box nature of the VMs. From this perspective, this paper proposes a novel machine learning-based performance prediction approach for applications running in the cloud. To achieve high-accuracy predictions for black-box VMs, the proposed method first identifies the running application inside the virtual machine. It then selects highly correlated runtime metrics as the input of the machine learning approach to accurately predict the performance level of the cloud application. Experimental results with state-of-the-art cloud benchmarks demonstrate that our proposed method outperforms existing prediction methods by more than 2× in terms of the worst prediction error. In addition, we successfully tackle the challenge of performance prediction for applications with variable workloads by introducing the performance degradation index, which other comparison methods fail to consider. The workflow versatility of the proposed approach has been verified with different modern servers and VM configurations.","PeriodicalId":13268,"journal":{"name":"IEEE Transactions on Sustainable Computing","volume":null,"pages":null},"PeriodicalIF":3.0,"publicationDate":"2024-01-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141965766","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-01-26DOI: 10.1109/TSUSC.2024.3358915
Aman Mishra;Yash Garg;Om Jee Pandey;Mahendra K. Shukla;Athanasios V. Vasilakos;Rajesh M. Hegde
At present, the centralized learning models, used for IoT applications generating large amount of data, face several challenges such as bandwidth scarcity, more energy consumption, increased uses of computing resources, poor connectivity, high computational complexity, reduced privacy, and large latency towards data transfer. In order to address the aforementioned challenges, Blockchain-Enabled Federated Learning Networks (BFLNs) emerged recently, which deal with trained model parameters only, rather than raw data. BFLNs provide enhanced security along with improved energy-efficiency and Quality-of-Service (QoS). However, BFLNs suffer with the challenges of exponential increased action space in deciding various parameter levels towards training and block generation. Motivated by aforementioned challenges of BFLNs, in this work, we are proposing an actor-critic Reinforcement Learning (RL) method to model the Machine Learning Model Owner (MLMO) in selecting the optimal set of parameter levels, addressing the challenges of exponential grow of action space in BFLNs. Further, due to the implicit entropy exploration, actor-critic RL method balances the exploration-exploitation trade-off and shows better performance than most off-policy methods, on large discrete action spaces. Therefore, in this work, considering the mobile scenario of the devices, MLMO decides the data and energy levels that the mobile devices use for the training and determine the block generation rate. This leads to minimized system latency and reduced overall cost, while achieving the target accuracy. Specifically, we have used Proximal Policy Optimization (PPO) as an on-policy actor-critic method with it's two variants, one based on Monte Carlo (MC) returns and another based on Generalized Advantage Estimate (GAE). We analyzed that PPO has better exploration and sample efficiency, lesser training time, and consistently higher cumulative rewards, when compared to off-policy Deep Q-Network (DQN).
{"title":"A Novel Resource Management Framework for Blockchain-Based Federated Learning in IoT Networks","authors":"Aman Mishra;Yash Garg;Om Jee Pandey;Mahendra K. Shukla;Athanasios V. Vasilakos;Rajesh M. Hegde","doi":"10.1109/TSUSC.2024.3358915","DOIUrl":"https://doi.org/10.1109/TSUSC.2024.3358915","url":null,"abstract":"At present, the centralized learning models, used for IoT applications generating large amount of data, face several challenges such as bandwidth scarcity, more energy consumption, increased uses of computing resources, poor connectivity, high computational complexity, reduced privacy, and large latency towards data transfer. In order to address the aforementioned challenges, Blockchain-Enabled Federated Learning Networks (BFLNs) emerged recently, which deal with trained model parameters only, rather than raw data. BFLNs provide enhanced security along with improved energy-efficiency and Quality-of-Service (QoS). However, BFLNs suffer with the challenges of exponential increased action space in deciding various parameter levels towards training and block generation. Motivated by aforementioned challenges of BFLNs, in this work, we are proposing an actor-critic Reinforcement Learning (RL) method to model the Machine Learning Model Owner (MLMO) in selecting the optimal set of parameter levels, addressing the challenges of exponential grow of action space in BFLNs. Further, due to the implicit entropy exploration, actor-critic RL method balances the exploration-exploitation trade-off and shows better performance than most off-policy methods, on large discrete action spaces. Therefore, in this work, considering the mobile scenario of the devices, MLMO decides the data and energy levels that the mobile devices use for the training and determine the block generation rate. This leads to minimized system latency and reduced overall cost, while achieving the target accuracy. Specifically, we have used Proximal Policy Optimization (PPO) as an on-policy actor-critic method with it's two variants, one based on Monte Carlo (MC) returns and another based on Generalized Advantage Estimate (GAE). We analyzed that PPO has better exploration and sample efficiency, lesser training time, and consistently higher cumulative rewards, when compared to off-policy Deep Q-Network (DQN).","PeriodicalId":13268,"journal":{"name":"IEEE Transactions on Sustainable Computing","volume":null,"pages":null},"PeriodicalIF":3.0,"publicationDate":"2024-01-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141965830","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-01-09DOI: 10.1109/TSUSC.2024.3351684
Jie Li;Yuhui Deng;Zhifeng Fan;Zijie Zhong;Geyong Min
The explosion of large-scale data has increased the scale and capacity of storage clusters in data centers, leading to huge power consumption issues. Cloud providers can effectively promote the energy efficiency of data centers by employing energy-aware data placement techniques, which primarily encompass storage cluster's power and cooling power. Traditional data placement approaches do not diminish the overall power consumption of the data center due to the heat recirculation effect between storage nodes. To fill this gap, we build an elaborate thermal-aware data center model. Then we propose two energy-efficient thermal-aware data placement strategies, ETDP-I and ETDP-II, to reduce the overall power consumption of the data center. The principle of our proposed algorithm is to utilize a greedy algorithm to calculate the optimal disk sequence at the minimum total power of the data center and then place the data into the optimal disk sequence. We implement these two strategies in a cloud computing simulation platform based on CloudSim. Experimental results unveil that ETDA-I and ETDP-II outperform MinTin-G and MinTout-G in terms of the supplied temperature of CRAC, storage nodes power, cooling cost, and total power consumption of the data center. In particular, ETDP-I and ETDP-II algorithms can save about 9.46 $%$ -38.93 $%$ of the overall power consumption compared to MinTout-G and MinTin-G algorithms.
{"title":"Towards Energy-Efficient and Thermal-Aware Data Placement for Storage Clusters","authors":"Jie Li;Yuhui Deng;Zhifeng Fan;Zijie Zhong;Geyong Min","doi":"10.1109/TSUSC.2024.3351684","DOIUrl":"https://doi.org/10.1109/TSUSC.2024.3351684","url":null,"abstract":"The explosion of large-scale data has increased the scale and capacity of storage clusters in data centers, leading to huge power consumption issues. Cloud providers can effectively promote the energy efficiency of data centers by employing energy-aware data placement techniques, which primarily encompass storage cluster's power and cooling power. Traditional data placement approaches do not diminish the overall power consumption of the data center due to the heat recirculation effect between storage nodes. To fill this gap, we build an elaborate thermal-aware data center model. Then we propose two energy-efficient thermal-aware data placement strategies, ETDP-I and ETDP-II, to reduce the overall power consumption of the data center. The principle of our proposed algorithm is to utilize a greedy algorithm to calculate the optimal disk sequence at the minimum total power of the data center and then place the data into the optimal disk sequence. We implement these two strategies in a cloud computing simulation platform based on CloudSim. Experimental results unveil that ETDA-I and ETDP-II outperform MinTin-G and MinTout-G in terms of the supplied temperature of CRAC, storage nodes power, cooling cost, and total power consumption of the data center. In particular, ETDP-I and ETDP-II algorithms can save about 9.46\u0000<inline-formula><tex-math>$%$</tex-math></inline-formula>\u0000-38.93\u0000<inline-formula><tex-math>$%$</tex-math></inline-formula>\u0000 of the overall power consumption compared to MinTout-G and MinTin-G algorithms.","PeriodicalId":13268,"journal":{"name":"IEEE Transactions on Sustainable Computing","volume":null,"pages":null},"PeriodicalIF":3.0,"publicationDate":"2024-01-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141965832","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-01-09DOI: 10.1109/TSUSC.2024.3351282
Tao Liu;Peng Li;Zhou Su;Mianxiong Dong
Graph neural networks (GNNs) have attracted significant research attention because of their impressive capability in dealing with graph-structure data, such as energy networks, that are crucial for sustainable computing. We find that the communication of data loading from main memory to GPUs is the main bottleneck of GNN inference because of redundant data loading. In this paper, we propose RAIN, an efficient GNN inference system for graph learning. There are two key designs. First, we explore the opportunity of conducting similar inference batches sequentially and reusing repeated nodes among adjacent batches to reduce redundant data loading. This method requires reordering the batches based on their similarity. However, comparing the similarity across a large number of inference batches is a difficult task with a high computational cost. Thus, we propose a local sensitive hash (LSH)-based clustering scheme to group similar batches together quickly without pair-wise comparison. Second, RAIN contains an efficient adaptive sampling strategy, allowing users to sample target nodes’ neighbors according to their degree. The number of sampled neighbors is proportional to the size of the node's degree. We conduct extensive experiments with various baselines. RAIN can achieve up to 6.8X acceleration, and the accuracy decrease is smaller than 0.1%.
{"title":"Efficient Inference of Graph Neural Networks Using Local Sensitive Hash","authors":"Tao Liu;Peng Li;Zhou Su;Mianxiong Dong","doi":"10.1109/TSUSC.2024.3351282","DOIUrl":"https://doi.org/10.1109/TSUSC.2024.3351282","url":null,"abstract":"Graph neural networks (GNNs) have attracted significant research attention because of their impressive capability in dealing with graph-structure data, such as energy networks, that are crucial for sustainable computing. We find that the communication of data loading from main memory to GPUs is the main bottleneck of GNN inference because of redundant data loading. In this paper, we propose RAIN, an efficient GNN inference system for graph learning. There are two key designs. First, we explore the opportunity of conducting similar inference batches sequentially and reusing repeated nodes among adjacent batches to reduce redundant data loading. This method requires reordering the batches based on their similarity. However, comparing the similarity across a large number of inference batches is a difficult task with a high computational cost. Thus, we propose a local sensitive hash (LSH)-based clustering scheme to group similar batches together quickly without pair-wise comparison. Second, RAIN contains an efficient adaptive sampling strategy, allowing users to sample target nodes’ neighbors according to their degree. The number of sampled neighbors is proportional to the size of the node's degree. We conduct extensive experiments with various baselines. RAIN can achieve up to 6.8X acceleration, and the accuracy decrease is smaller than 0.1%.","PeriodicalId":13268,"journal":{"name":"IEEE Transactions on Sustainable Computing","volume":null,"pages":null},"PeriodicalIF":3.9,"publicationDate":"2024-01-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141264414","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-01-05DOI: 10.1109/TSUSC.2024.3350343
Yuan Su;Yuheng Wang;Jiliang Li;Zhou Su;Witold Pedrycz;Qinnan Hu
The Public Key Infrastructure (PKI) system is the cornerstone of today’s security communications. All users in the service domain covered by the same PKI system are able to authenticate each other before exchanging messages. However, there is identity isolation in different domains, making the identity of users in different domains cannot be recognized by PKI systems in other domains. To achieve cross-domain authentication, the consortium blockchain system is leveraged in the existing schemes. Unfortunately, the consortium blockchain-based authentication schemes have the following challenges: high cost, privacy concerns, scalability and economic unsustainability. To solve these challenges, we propose a scalable and privacy-preserving cross-domain authentication scheme called Bifrost-Auth. Firstly, Bifrost-Auth is designed to use a decentralized oracle to directly interact with blockchains in different domains instead of maintaining a consortium blockchain and enables mutual authentication for users lying in different domains. Secondly, users can succinctly authenticate their membership of the domain by the accumulator technique, where the membership proof is turned into zero knowledge to protect users’ privacy. Finally, Bifrost-Auth is proven to be secure against various attacks, and thorough experiments are carried out and demonstrate the security and efficiency of Bifrost-Auth.
{"title":"Oracle Based Privacy-Preserving Cross-Domain Authentication Scheme","authors":"Yuan Su;Yuheng Wang;Jiliang Li;Zhou Su;Witold Pedrycz;Qinnan Hu","doi":"10.1109/TSUSC.2024.3350343","DOIUrl":"https://doi.org/10.1109/TSUSC.2024.3350343","url":null,"abstract":"The Public Key Infrastructure (PKI) system is the cornerstone of today’s security communications. All users in the service domain covered by the same PKI system are able to authenticate each other before exchanging messages. However, there is identity isolation in different domains, making the identity of users in different domains cannot be recognized by PKI systems in other domains. To achieve cross-domain authentication, the consortium blockchain system is leveraged in the existing schemes. Unfortunately, the consortium blockchain-based authentication schemes have the following challenges: high cost, privacy concerns, scalability and economic unsustainability. To solve these challenges, we propose a scalable and privacy-preserving cross-domain authentication scheme called Bifrost-Auth. Firstly, Bifrost-Auth is designed to use a decentralized oracle to directly interact with blockchains in different domains instead of maintaining a consortium blockchain and enables mutual authentication for users lying in different domains. Secondly, users can succinctly authenticate their membership of the domain by the accumulator technique, where the membership proof is turned into zero knowledge to protect users’ privacy. Finally, Bifrost-Auth is proven to be secure against various attacks, and thorough experiments are carried out and demonstrate the security and efficiency of Bifrost-Auth.","PeriodicalId":13268,"journal":{"name":"IEEE Transactions on Sustainable Computing","volume":null,"pages":null},"PeriodicalIF":3.0,"publicationDate":"2024-01-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141965768","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
5G vehicular networks provide abundant multimedia services among mobile vehicles. However, due to the mobility of vehicles, large-scale mobile traffic poses a challenge to the core network load and transmission latency. It is difficult for existing solutions to guarantee the quality of service (QoS) of vehicular networks. Besides, the sensitivity of vehicle trajectories also brings privacy concerns in vehicular networks. To address these problems, we propose a privacy-preserving and trajectory clustering-based framework for cooperative caching optimization (PTCC) in vehicular networks, which includes two tasks. Specifically, in the first task, we first apply differential privacy technologies to add noise to vehicle trajectories. In addition, a data aggregation model is provided to make the trade-off between aggregation accuracy and privacy protection. In order to analyze similar behavioral vehicles, trajectory clustering is then achieved by utilizing machine learning algorithms. In the second task, we construct a cooperative caching objective function with the transmission latency. Afterwards, the multi-agent deep Q network (MADQN) is leveraged to obtain the goal of caching optimization, which can achieve low delay. Finally, extensive simulation results verify that our framework respectively improves the QoS up to 9.8% and 12.8% with different file numbers and caching capacities, compared with other state-of-the-art solutions.
{"title":"PTCC: A Privacy-Preserving and Trajectory Clustering-Based Approach for Cooperative Caching Optimization in Vehicular Networks","authors":"Tengfei Cao;Zizhen Zhang;Xiaoying Wang;Han Xiao;Changqiao Xu","doi":"10.1109/TSUSC.2024.3350386","DOIUrl":"https://doi.org/10.1109/TSUSC.2024.3350386","url":null,"abstract":"5G vehicular networks provide abundant multimedia services among mobile vehicles. However, due to the mobility of vehicles, large-scale mobile traffic poses a challenge to the core network load and transmission latency. It is difficult for existing solutions to guarantee the quality of service (QoS) of vehicular networks. Besides, the sensitivity of vehicle trajectories also brings privacy concerns in vehicular networks. To address these problems, we propose a privacy-preserving and trajectory clustering-based framework for cooperative caching optimization (PTCC) in vehicular networks, which includes two tasks. Specifically, in the first task, we first apply differential privacy technologies to add noise to vehicle trajectories. In addition, a data aggregation model is provided to make the trade-off between aggregation accuracy and privacy protection. In order to analyze similar behavioral vehicles, trajectory clustering is then achieved by utilizing machine learning algorithms. In the second task, we construct a cooperative caching objective function with the transmission latency. Afterwards, the multi-agent deep Q network (MADQN) is leveraged to obtain the goal of caching optimization, which can achieve low delay. Finally, extensive simulation results verify that our framework respectively improves the QoS up to 9.8% and 12.8% with different file numbers and caching capacities, compared with other state-of-the-art solutions.","PeriodicalId":13268,"journal":{"name":"IEEE Transactions on Sustainable Computing","volume":null,"pages":null},"PeriodicalIF":3.0,"publicationDate":"2024-01-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141965831","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-01-05DOI: 10.1109/TSUSC.2024.3350040
Meng Chen;Hengzhu Liu;Huanhuan Chi;Ping Xiong
Multi-party collaborative learning has become a paradigm for large-scale knowledge discovery in the era of Big Data. As a typical form of collaborative learning, federated learning (FL) has received widespread research attention in recent years. In practice, however, FL faces a range of challenges such as objective inconsistency, communication and synchronization issues, due to the heterogeneity in the clients’ local datasets and devices. In this paper, we propose EnsembleFed, a novel ensemble framework for heterogeneous FL. The proposed framework first allows each client to train a local model with full autonomy and without having to consider the heterogeneity of local datasets. The confidence scores of training samples output by each local model are then perturbed to defend against membership inference attacks, after which they are submitted to the server for use in constructing the global model. We apply a GAN-based method to generate calibrated noise for confidence perturbation. Benefiting from the ensemble framework, EnsembleFed disengages from the restriction of real-time synchronization and achieves collaborative learning with lower communication costs than traditional FL. Experiments on real-world datasets demonstrate that the proposed EnsembleFed can significantly improve the performance of the global model while also effectively defending against membership inference attacks.
{"title":"Heterogeneous Ensemble Federated Learning With GAN-Based Privacy Preservation","authors":"Meng Chen;Hengzhu Liu;Huanhuan Chi;Ping Xiong","doi":"10.1109/TSUSC.2024.3350040","DOIUrl":"https://doi.org/10.1109/TSUSC.2024.3350040","url":null,"abstract":"Multi-party collaborative learning has become a paradigm for large-scale knowledge discovery in the era of Big Data. As a typical form of collaborative learning, federated learning (FL) has received widespread research attention in recent years. In practice, however, FL faces a range of challenges such as objective inconsistency, communication and synchronization issues, due to the heterogeneity in the clients’ local datasets and devices. In this paper, we propose EnsembleFed, a novel ensemble framework for heterogeneous FL. The proposed framework first allows each client to train a local model with full autonomy and without having to consider the heterogeneity of local datasets. The confidence scores of training samples output by each local model are then perturbed to defend against membership inference attacks, after which they are submitted to the server for use in constructing the global model. We apply a GAN-based method to generate calibrated noise for confidence perturbation. Benefiting from the ensemble framework, EnsembleFed disengages from the restriction of real-time synchronization and achieves collaborative learning with lower communication costs than traditional FL. Experiments on real-world datasets demonstrate that the proposed EnsembleFed can significantly improve the performance of the global model while also effectively defending against membership inference attacks.","PeriodicalId":13268,"journal":{"name":"IEEE Transactions on Sustainable Computing","volume":null,"pages":null},"PeriodicalIF":3.0,"publicationDate":"2024-01-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141965767","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
京公网安备 11010802042870号
京ICP备2023020795号-1
扫码关注我们
求助内容：
应助结果提醒方式：