Pub Date : 2020-11-01DOI: 10.1109/ICDCS47774.2020.00016
Zhaofeng Zhang, Yue Chen, Junshan Zhang
In order to meet the real-time performance requirements, intelligent decisions in many IoT applications must take place right here right now at the network edge. The conventional cloud-based learning approach would not be able to keep up with the demands in achieving edge intelligence in these applications. Nevertheless, pushing the artificial intelligence (AI) frontier to achieve edge intelligence is highly nontrivial due to the constrained computing resources and limited training data at the network edge. To tackle these challenges, we develop a distributionally robust optimization (DRO)-based edge learning algorithm, where the uncertainty model is constructed to foster the synergy of cloud knowledge transfer and local training. Specifically, the knowledge transferred from the cloud is in the form of a Dirichlet process prior distribution for the edge model parameters, and the edge device further constructs an uncertainty set centered around the empirical distribution of its local samples to capture the information of local data processing. The edge learning DRO problem, subject to the above two distributional uncertainty constraints, is then recast as an equivalent single-layer optimization problem using a duality approach. We then use an Expectation-Maximization (EM) algorithm-inspired method to derive a convex relaxation, based on which we devise algorithms to learn the edge model parameters. Finally, extensive experiments are implemented to showcase the performance gain over standard learning approaches using local edge data only.
{"title":"Distributionally Robust Edge Learning with Dirichlet Process Prior","authors":"Zhaofeng Zhang, Yue Chen, Junshan Zhang","doi":"10.1109/ICDCS47774.2020.00016","DOIUrl":"https://doi.org/10.1109/ICDCS47774.2020.00016","url":null,"abstract":"In order to meet the real-time performance requirements, intelligent decisions in many IoT applications must take place right here right now at the network edge. The conventional cloud-based learning approach would not be able to keep up with the demands in achieving edge intelligence in these applications. Nevertheless, pushing the artificial intelligence (AI) frontier to achieve edge intelligence is highly nontrivial due to the constrained computing resources and limited training data at the network edge. To tackle these challenges, we develop a distributionally robust optimization (DRO)-based edge learning algorithm, where the uncertainty model is constructed to foster the synergy of cloud knowledge transfer and local training. Specifically, the knowledge transferred from the cloud is in the form of a Dirichlet process prior distribution for the edge model parameters, and the edge device further constructs an uncertainty set centered around the empirical distribution of its local samples to capture the information of local data processing. The edge learning DRO problem, subject to the above two distributional uncertainty constraints, is then recast as an equivalent single-layer optimization problem using a duality approach. We then use an Expectation-Maximization (EM) algorithm-inspired method to derive a convex relaxation, based on which we devise algorithms to learn the edge model parameters. Finally, extensive experiments are implemented to showcase the performance gain over standard learning approaches using local edge data only.","PeriodicalId":158630,"journal":{"name":"2020 IEEE 40th International Conference on Distributed Computing Systems (ICDCS)","volume":"71 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114266589","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}
Pub Date : 2020-11-01DOI: 10.1109/ICDCS47774.2020.00154
Yue Fu, M. Au, Rong Du, Haibo Hu, Dagang Li
Password-based authentication is essential to any online service. It is normally powered by a database of user credentials, for example a RADIUS server. However, even with various indexing techniques (e.g., B+-tree), password-based authentication can still be resource-consuming on large-scale systems (e.g., Internet and IoT), and is thus vulnerable to distributed denial-of-service (DDoS) attacks.In this paper, we propose a cloud-based firewall that uses Bloom filters to pre-screen and reject suspicious requests with wrong password before they reach the authentication server. The main challenge is the security of the firewall because it can be operated by a third party, so the Bloom filters might be accessed by adversaries to assist their brute-force password guessing.To ensure security, we start with the assumption of trusted cloud server and design a key-based semantic secure Bloom filter (KSSBF) for the best efficiency. We then design a generically secure Bloom filter (GSBF) for non-trusted cloud servers, which is key-independent and with strictly provable security. Through theoretical and empirical analysis, we show both of them can mitigate malicious requests without compromising the security of passwords.
{"title":"Cloud Password Shield: A Secure Cloud-based Firewall against DDoS on Authentication Servers","authors":"Yue Fu, M. Au, Rong Du, Haibo Hu, Dagang Li","doi":"10.1109/ICDCS47774.2020.00154","DOIUrl":"https://doi.org/10.1109/ICDCS47774.2020.00154","url":null,"abstract":"Password-based authentication is essential to any online service. It is normally powered by a database of user credentials, for example a RADIUS server. However, even with various indexing techniques (e.g., B+-tree), password-based authentication can still be resource-consuming on large-scale systems (e.g., Internet and IoT), and is thus vulnerable to distributed denial-of-service (DDoS) attacks.In this paper, we propose a cloud-based firewall that uses Bloom filters to pre-screen and reject suspicious requests with wrong password before they reach the authentication server. The main challenge is the security of the firewall because it can be operated by a third party, so the Bloom filters might be accessed by adversaries to assist their brute-force password guessing.To ensure security, we start with the assumption of trusted cloud server and design a key-based semantic secure Bloom filter (KSSBF) for the best efficiency. We then design a generically secure Bloom filter (GSBF) for non-trusted cloud servers, which is key-independent and with strictly provable security. Through theoretical and empirical analysis, we show both of them can mitigate malicious requests without compromising the security of passwords.","PeriodicalId":158630,"journal":{"name":"2020 IEEE 40th International Conference on Distributed Computing Systems (ICDCS)","volume":"42 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134311482","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}
Pub Date : 2020-11-01DOI: 10.1109/ICDCS47774.2020.00031
Xianglin Wei, Li Li, Chaogang Tang, M. Doroslovački, S. Subramaniam
Coping with diverse channel access attacks (CAAs) has been a major obstacle to realize the full potential of wireless networks as a basic building block of smart applications. Identifying and classifying different types of CAAs in a timely manner is a great challenge because of the inherently shared nature and randomness of the wireless medium. To overcome the difficulties encountered in existing methods, such as long latency, high data collection overhead, and limited applicable range, a deep learning-based CAA detection framework is proposed in this paper. First, we show the challenges of CAA classification by analyzing the impacts of CAAs on wireless network performance using an event-driven network simulator. Second, a state-transition model is built for the channel access process at a node, whose output sequences characterize the changing patterns of the node’s transmission status in different CAA scenarios. Third, a deep learning-based CAA classification framework is presented, which takes state transition sequences of a node as input and outputs predicted CAA types. The performance of three deep neural networks, i.e., fully-connected, convolutional, and Long Short-Term Memory (LSTM) network, for classifying CAAs are evaluated under our CAA classification framework in five CAA scenarios and the normal scenario without CAA. Experimental results show that LSTM outperforms the other two neural network architectures, and its CAA classification accuracy is higher than 95%. We successfully transferred the learned LSTM model to classify CAAs on other nodes in the same network and the nodes in other networks, which verifies the generality of our proposed framework.
{"title":"Classification of Channel Access Attacks in Wireless Networks: A Deep Learning Approach","authors":"Xianglin Wei, Li Li, Chaogang Tang, M. Doroslovački, S. Subramaniam","doi":"10.1109/ICDCS47774.2020.00031","DOIUrl":"https://doi.org/10.1109/ICDCS47774.2020.00031","url":null,"abstract":"Coping with diverse channel access attacks (CAAs) has been a major obstacle to realize the full potential of wireless networks as a basic building block of smart applications. Identifying and classifying different types of CAAs in a timely manner is a great challenge because of the inherently shared nature and randomness of the wireless medium. To overcome the difficulties encountered in existing methods, such as long latency, high data collection overhead, and limited applicable range, a deep learning-based CAA detection framework is proposed in this paper. First, we show the challenges of CAA classification by analyzing the impacts of CAAs on wireless network performance using an event-driven network simulator. Second, a state-transition model is built for the channel access process at a node, whose output sequences characterize the changing patterns of the node’s transmission status in different CAA scenarios. Third, a deep learning-based CAA classification framework is presented, which takes state transition sequences of a node as input and outputs predicted CAA types. The performance of three deep neural networks, i.e., fully-connected, convolutional, and Long Short-Term Memory (LSTM) network, for classifying CAAs are evaluated under our CAA classification framework in five CAA scenarios and the normal scenario without CAA. Experimental results show that LSTM outperforms the other two neural network architectures, and its CAA classification accuracy is higher than 95%. We successfully transferred the learned LSTM model to classify CAAs on other nodes in the same network and the nodes in other networks, which verifies the generality of our proposed framework.","PeriodicalId":158630,"journal":{"name":"2020 IEEE 40th International Conference on Distributed Computing Systems (ICDCS)","volume":"3 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133435430","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}
Pub Date : 2020-11-01DOI: 10.1109/icdcs47774.2020.00001
{"title":"[Title page i]","authors":"","doi":"10.1109/icdcs47774.2020.00001","DOIUrl":"https://doi.org/10.1109/icdcs47774.2020.00001","url":null,"abstract":"","PeriodicalId":158630,"journal":{"name":"2020 IEEE 40th International Conference on Distributed Computing Systems (ICDCS)","volume":"42 12","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114121950","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}
Pub Date : 2020-11-01DOI: 10.1109/ICDCS47774.2020.00066
Hisham Alhulayyil, Kittipat Apicharttrisorn, Jiasi Chen, K. Sundaresan, Samet Oymak, S. Krishnamurthy
Power Line Communication (PLC) based WiFi extenders can improve WiFi coverage in homes and enterprises. Unlike in traditional WiFi networks which use an underlying high data rate Ethernet backhaul, a PLC backhaul may not support high data rates. Specifically, our measurements show that arbitrarily affiliating users to PLC-WiFi extenders or based on their WiFi channel qualities alone may lead to poor network performance due to the differences in PLC link capacities. Thus, in this paper we build a framework, WOLT, to solve the problem of assigning users to the appropriate PLC-WiFi extenders to increase the aggregate network throughput in an enterprise setting, where one may expect a relatively large number of power outlets. WOLT accounts for both the qualities of the two concatenated links viz., the PLC and WiFi links. It hinges on estimating the best capacity offered by the PLC links, and accounting for these while assigning users. It incorporates a polynomial-time algorithm that assigns only a subset of the users to maximize the aggregate throughput on the PLC links, and then assigns the remaining users such that the degradation in the aggregate throughput is minimized. WOLT is evaluated through simulations and real testbed experiments with commodity PLCWiFi extenders, and improves aggregate throughput by more than 2.5x compared to a greedy user association baseline.
{"title":"WOLT: Auto-Configuration of Integrated Enterprise PLC-WiFi Networks","authors":"Hisham Alhulayyil, Kittipat Apicharttrisorn, Jiasi Chen, K. Sundaresan, Samet Oymak, S. Krishnamurthy","doi":"10.1109/ICDCS47774.2020.00066","DOIUrl":"https://doi.org/10.1109/ICDCS47774.2020.00066","url":null,"abstract":"Power Line Communication (PLC) based WiFi extenders can improve WiFi coverage in homes and enterprises. Unlike in traditional WiFi networks which use an underlying high data rate Ethernet backhaul, a PLC backhaul may not support high data rates. Specifically, our measurements show that arbitrarily affiliating users to PLC-WiFi extenders or based on their WiFi channel qualities alone may lead to poor network performance due to the differences in PLC link capacities. Thus, in this paper we build a framework, WOLT, to solve the problem of assigning users to the appropriate PLC-WiFi extenders to increase the aggregate network throughput in an enterprise setting, where one may expect a relatively large number of power outlets. WOLT accounts for both the qualities of the two concatenated links viz., the PLC and WiFi links. It hinges on estimating the best capacity offered by the PLC links, and accounting for these while assigning users. It incorporates a polynomial-time algorithm that assigns only a subset of the users to maximize the aggregate throughput on the PLC links, and then assigns the remaining users such that the degradation in the aggregate throughput is minimized. WOLT is evaluated through simulations and real testbed experiments with commodity PLCWiFi extenders, and improves aggregate throughput by more than 2.5x compared to a greedy user association baseline.","PeriodicalId":158630,"journal":{"name":"2020 IEEE 40th International Conference on Distributed Computing Systems (ICDCS)","volume":"99 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114375670","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}
Pub Date : 2020-11-01DOI: 10.1109/ICDCS47774.2020.00196
Z. Song, E. Tilevich
Mobile and energy harvesting devices increasingly provide resources for edge environments. These devices’ mobility and limited energy budgets may cause failures and poor performance. The reliability and efficiency of edge services can be improved with equivalent microservices that satisfy application requirements by different means: execute equivalent microservices in the predefined patterns of fail-over to minimize execution costs or speculative parallelism to reduce latency. However, given the vast dissimilarities in resource availability and capability across edge environments, being limited to these predefined patterns when implementing edge services causes inconsistent QoS. To address this problem, we provide QoS-consistent edge services by customizing the execution of equivalent microservices. Our system estimates the environment-specific QoS of equivalent microservices and dynamically generates execution strategies that best satisfy given QoS requirements. We evaluate the effectiveness and performance of our system via simulations and benchmarks with realistic edge deployments. Our approach consistently out-performs the predefined execution patterns in satisfying the QoS requirements in unreliable and dynamic edge environments.
{"title":"Win with What You Have: QoS-Consistent Edge Services with Unreliable and Dynamic Resources","authors":"Z. Song, E. Tilevich","doi":"10.1109/ICDCS47774.2020.00196","DOIUrl":"https://doi.org/10.1109/ICDCS47774.2020.00196","url":null,"abstract":"Mobile and energy harvesting devices increasingly provide resources for edge environments. These devices’ mobility and limited energy budgets may cause failures and poor performance. The reliability and efficiency of edge services can be improved with equivalent microservices that satisfy application requirements by different means: execute equivalent microservices in the predefined patterns of fail-over to minimize execution costs or speculative parallelism to reduce latency. However, given the vast dissimilarities in resource availability and capability across edge environments, being limited to these predefined patterns when implementing edge services causes inconsistent QoS. To address this problem, we provide QoS-consistent edge services by customizing the execution of equivalent microservices. Our system estimates the environment-specific QoS of equivalent microservices and dynamically generates execution strategies that best satisfy given QoS requirements. We evaluate the effectiveness and performance of our system via simulations and benchmarks with realistic edge deployments. Our approach consistently out-performs the predefined execution patterns in satisfying the QoS requirements in unreliable and dynamic edge environments.","PeriodicalId":158630,"journal":{"name":"2020 IEEE 40th International Conference on Distributed Computing Systems (ICDCS)","volume":"23 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114524558","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}
Pub Date : 2020-11-01DOI: 10.1109/ICDCS47774.2020.00139
Li Yan, Haiying Shen, Liuwang Kang, Juanjuan Zhao, Chengzhong Xu
With ever increasing concerns on environmental issues caused by gasoline fuel based vehicles, electric vehicles (EVs) have attracted more and more attention from governments, industries, and customers [1] . The recent advancements in EVs have great potential to create a more environmentally friendly smart city. However, due to limited battery capacity, most current mainstream EVs still have quite limited driving range (e.g., 100 miles) [2] . How to ensure the continuous running of EVs on a large-scale road network (e.g., metropolitan city, interstate) becomes a major concern.
{"title":"MobiCharger: Optimal Scheduling for Cooperative EV-to-EV Dynamic Wireless Charging","authors":"Li Yan, Haiying Shen, Liuwang Kang, Juanjuan Zhao, Chengzhong Xu","doi":"10.1109/ICDCS47774.2020.00139","DOIUrl":"https://doi.org/10.1109/ICDCS47774.2020.00139","url":null,"abstract":"With ever increasing concerns on environmental issues caused by gasoline fuel based vehicles, electric vehicles (EVs) have attracted more and more attention from governments, industries, and customers [1] . The recent advancements in EVs have great potential to create a more environmentally friendly smart city. However, due to limited battery capacity, most current mainstream EVs still have quite limited driving range (e.g., 100 miles) [2] . How to ensure the continuous running of EVs on a large-scale road network (e.g., metropolitan city, interstate) becomes a major concern.","PeriodicalId":158630,"journal":{"name":"2020 IEEE 40th International Conference on Distributed Computing Systems (ICDCS)","volume":"11 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115162717","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}
The effectiveness of applying key-value store mechanisms to manage metadata of file systems has been demonstrated recently. However, traditional indirect metadata indexing schemes are not in concert with modern key-value data structures, which could degrade the performance of a KV-embedded file system due to the overhead of hierarchical path queries. In this paper, we propose FILT, a proof-of-concept file system middleware that can solve this problem by employing flat indexing. FILT exploits the benefits of both flat indexing and LSM-tree structure to eliminate redundant path lookups. Our extensive performance evaluation studies show that FILT can offer up to 5.8x performance gain compared with sophisticated local file systems.
{"title":"FILT: Optimizing KV-Embedded File Systems through Flat Indexing","authors":"Chen Chen, Tongliang Deng, Jian Zhang, Yanliang Zou, Xiaomin Zhu, Shu Yin","doi":"10.1109/ICDCS47774.2020.00150","DOIUrl":"https://doi.org/10.1109/ICDCS47774.2020.00150","url":null,"abstract":"The effectiveness of applying key-value store mechanisms to manage metadata of file systems has been demonstrated recently. However, traditional indirect metadata indexing schemes are not in concert with modern key-value data structures, which could degrade the performance of a KV-embedded file system due to the overhead of hierarchical path queries. In this paper, we propose FILT, a proof-of-concept file system middleware that can solve this problem by employing flat indexing. FILT exploits the benefits of both flat indexing and LSM-tree structure to eliminate redundant path lookups. Our extensive performance evaluation studies show that FILT can offer up to 5.8x performance gain compared with sophisticated local file systems.","PeriodicalId":158630,"journal":{"name":"2020 IEEE 40th International Conference on Distributed Computing Systems (ICDCS)","volume":"19 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126047701","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}
Pub Date : 2020-11-01DOI: 10.1109/ICDCS47774.2020.00017
Yang Liu, Zhuo Ma, Ximeng Liu, Siqi Ma, S. Nepal, R. Deng, K. Ren
Recently, Google and other 24 institutions proposed a series of open challenges towards federated learning (FL), which include application expansion and homomorphic encryption (HE). The former aims to expand the applicable machine learning models of FL. The latter focuses on who holds the secret key when applying HE to FL. For the naive HE scheme, the server is set to master the secret key. Such a setting causes a serious problem that if the server does not conduct aggregation before decryption, a chance is left for the server to access the user’s update. Inspired by the two challenges, we propose FEDXGB, a federated extreme gradient boosting (XGBoost) scheme supporting forced aggregation. FEDXGB mainly achieves the following two breakthroughs. First, FEDXGB involves a new HE based secure aggregation scheme for FL. By combining the advantages of secret sharing and homomorphic encryption, the algorithm can solve the second challenge mentioned above, and is robust to the user dropout. Then, FEDXGB extends FL to a new machine learning model by applying the secure aggregation scheme to the classification and regression tree building of XGBoost. Moreover, we conduct a comprehensive theoretical analysis and extensive experiments to evaluate the security, effectiveness, and efficiency of FEDXGB. The results indicate that FEDXGB achieves less than 1% accuracy loss compared with the original XGBoost, and can provide about 23.9% runtime and 33.3% communication reduction for HE based model update aggregation of FL.
{"title":"Boosting Privately: Federated Extreme Gradient Boosting for Mobile Crowdsensing","authors":"Yang Liu, Zhuo Ma, Ximeng Liu, Siqi Ma, S. Nepal, R. Deng, K. Ren","doi":"10.1109/ICDCS47774.2020.00017","DOIUrl":"https://doi.org/10.1109/ICDCS47774.2020.00017","url":null,"abstract":"Recently, Google and other 24 institutions proposed a series of open challenges towards federated learning (FL), which include application expansion and homomorphic encryption (HE). The former aims to expand the applicable machine learning models of FL. The latter focuses on who holds the secret key when applying HE to FL. For the naive HE scheme, the server is set to master the secret key. Such a setting causes a serious problem that if the server does not conduct aggregation before decryption, a chance is left for the server to access the user’s update. Inspired by the two challenges, we propose FEDXGB, a federated extreme gradient boosting (XGBoost) scheme supporting forced aggregation. FEDXGB mainly achieves the following two breakthroughs. First, FEDXGB involves a new HE based secure aggregation scheme for FL. By combining the advantages of secret sharing and homomorphic encryption, the algorithm can solve the second challenge mentioned above, and is robust to the user dropout. Then, FEDXGB extends FL to a new machine learning model by applying the secure aggregation scheme to the classification and regression tree building of XGBoost. Moreover, we conduct a comprehensive theoretical analysis and extensive experiments to evaluate the security, effectiveness, and efficiency of FEDXGB. The results indicate that FEDXGB achieves less than 1% accuracy loss compared with the original XGBoost, and can provide about 23.9% runtime and 33.3% communication reduction for HE based model update aggregation of FL.","PeriodicalId":158630,"journal":{"name":"2020 IEEE 40th International Conference on Distributed Computing Systems (ICDCS)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129377659","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}
Pub Date : 2020-11-01DOI: 10.1109/ICDCS47774.2020.00036
Phu Lai, Qiang He, Guangming Cui, Feifei Chen, Mohamed Abdelrazek, J. Grundy, J. Hosking, Yun Yang
As many applications and services are moving towards a more human-centered design, app vendors are taking the quality of experience (QoE) increasingly seriously. End-to-end latency is a key factor that determines the QoE experienced by users, especially for latency-sensitive applications such as online gaming, health care, critical warning systems and so on. Recently, edge computing has emerged as a promising solution to the high latency problem. In an edge computing environment, edge servers are deployed at cellular base stations, offering processing power and low network latency to users within their geographic proximity. In this paper, we tackle the user allocation problem in edge computing from an app vendor's perspective, where the vendor needs to decide which edge servers to serve which users in a specific area. Also, the vendor must consider the various levels of quality of service (QoS) for its users. Each QoS level results in a different QoE level; thus, the app vendor needs to decide the QoS level for each user so that the overall user experience is maximized. To tackle the NP-hardness of this problem, we formulate it as a potential game then propose QoEGame, an effective and efficient game-theoretic approach that admits a Nash equilibrium as a solution to the user allocation problem. Being a distributed algorithm, QoEGame is able to fully utilize the distributed nature of edge computing. Finally, we theoretically and empirically evaluate the performance of QoEGame, which is illustrated to be significantly better than the state of the art and other baseline approaches.
{"title":"Quality of Experience-Aware User Allocation in Edge Computing Systems: A Potential Game","authors":"Phu Lai, Qiang He, Guangming Cui, Feifei Chen, Mohamed Abdelrazek, J. Grundy, J. Hosking, Yun Yang","doi":"10.1109/ICDCS47774.2020.00036","DOIUrl":"https://doi.org/10.1109/ICDCS47774.2020.00036","url":null,"abstract":"As many applications and services are moving towards a more human-centered design, app vendors are taking the quality of experience (QoE) increasingly seriously. End-to-end latency is a key factor that determines the QoE experienced by users, especially for latency-sensitive applications such as online gaming, health care, critical warning systems and so on. Recently, edge computing has emerged as a promising solution to the high latency problem. In an edge computing environment, edge servers are deployed at cellular base stations, offering processing power and low network latency to users within their geographic proximity. In this paper, we tackle the user allocation problem in edge computing from an app vendor's perspective, where the vendor needs to decide which edge servers to serve which users in a specific area. Also, the vendor must consider the various levels of quality of service (QoS) for its users. Each QoS level results in a different QoE level; thus, the app vendor needs to decide the QoS level for each user so that the overall user experience is maximized. To tackle the NP-hardness of this problem, we formulate it as a potential game then propose QoEGame, an effective and efficient game-theoretic approach that admits a Nash equilibrium as a solution to the user allocation problem. Being a distributed algorithm, QoEGame is able to fully utilize the distributed nature of edge computing. Finally, we theoretically and empirically evaluate the performance of QoEGame, which is illustrated to be significantly better than the state of the art and other baseline approaches.","PeriodicalId":158630,"journal":{"name":"2020 IEEE 40th International Conference on Distributed Computing Systems (ICDCS)","volume":"41 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128483752","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}
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：