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- Al-Saedi, Ahmed Abbas Mohsin, 1980-
(författare)
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Resource-Aware and Personalized Federated Learning via Clustering Analysis
- 2024
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Doktorsavhandling (övrigt vetenskapligt/konstnärligt)abstract
- Today’s advancement in Artificial Intelligence (AI) enables training Machine Learning (ML) models on the daily-produced data by connected edge devices. To make the most of the data stored on the device, conventional ML approaches require gathering all individual data sets and transferring them to a central location to train a common model. However, centralizing data incurs significant costs related to communication, network resource utilization, high volume of traffic, and privacy issues. To address the aforementioned challenges, Federated Learning (FL) is employed as a novel approach to train a shared model on decentralized edge devices while preserving privacy. Despite the significant potential of FL, it still requires considerable resources such as time, computational power, energy, and bandwidth availability. More importantly, the computational capabilities of the training devices may vary over time. Furthermore, the devices involved in the training process of FL may have distinct training datasets that differ in terms of their size and distribution. As a result of this, the convergence of the FL models may become unstable and slow. These differences can influence the FL process and ultimately lead to suboptimal model performance within a heterogeneous federated network.In this thesis, we have tackled several of the aforementioned challenges. Initially, a FL algorithm is proposed that utilizes cluster analysis to address the problem of communication overhead. This issue poses a major bottleneck in FL, particularly for complex models, large-scale applications, and frequent updates. The next research conducted in this thesis involved extending the previous study to include wireless networks (WNs). In WSNs, achieving energy-efficient transmission is a significant challenge due to their limited resources. This has motivated us to continue with a comprehensive overview and classification of the latest advancements in context-aware edge-based AI models, with a specific emphasis on sensor networks. The review has also investigated the associated challenges and motivations for adopting AI techniques, along with an evaluation of current areas of research that need further investigation. To optimize the aggregation of the FL model and alleviate communication expenses, the initial study addressing communication overhead is extended to include a FL-based cluster optimization approach. Furthermore, to reduce the detrimental effect caused by data heterogeneity among edge devices on FL, a new study of group-personalized FL models has been conducted. Finally, taking inspiration from the previously mentioned FL models, techniques for assessing clients' contribution by monitoring and evaluating their behavior during training are proposed. In comparison with the most existing contribution evaluation solutions, the proposed techniques do not require significant computational resources.The FL algorithms presented in this thesis are assessed on a range of real-world datasets. The extensive experiments demonstrated that the proposed FL techniques are effective and robust. These techniques improve communication efficiency, resource utilization, model convergence speed, and aggregation efficiency, and also reduce data heterogeneity when compared to other state-of-the-art methods.
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- Al-Zubaidy, Hussein Mohammed, et al.
(författare)
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Performance of in-network processing for visual analysis in wireless sensor networks
- 2015
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Ingår i: Proceedings of 2015 14th IFIP Networking Conference, IFIP Networking 2015. - : IEEE conference proceedings. - 9783901882685
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Konferensbidrag (refereegranskat)abstract
- Nodes in a sensor network are traditionally used for sensing and data forwarding. However, with the increase of their computational capability, they can be used for in-network data processing, leading to a potential increase of the quality of the networked applications as well as the network lifetime. Visual analysis in sensor networks is a prominent example where the processing power of the network nodes needs to be leveraged to meet the frame rate and the processing delay requirements of common visual analysis applications. The modeling of the end-to-end performance for such networks is, however, challenging, because in-network processing violates the flow conservation law, which is the basis for most queuing analysis. In this work we propose to solve this methodological challenge through appropriately scaling the arrival and the service processes, and we develop probabilistic performance bounds using stochastic network calculus. We use the developed model to determine the main performance bottlenecks of networked visual processing. Our numerical results show that an end-to-end delay of 2-3 frame length is obtained with violation probability in the order of 10-6. Simulation shows that the obtained bounds overestimates the end-to-end delay by no more than 10%.
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- Al-Zubaidy, Hussein, et al.
(författare)
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Reliable Video Streaming With Strict Playout Deadline in Multihop Wireless Networks
- 2017
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Ingår i: IEEE transactions on multimedia. - : IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC. - 1520-9210 .- 1941-0077. ; 19:10, s. 2238-2251
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Tidskriftsartikel (refereegranskat)abstract
- Motivated by emerging vision-based intelligent services, we consider the problem of rate adaptation for high-quality and low-delay visual information delivery over wireless networks using scalable video coding. Rate adaptation in this setting is inherently challenging due to the interplay between the variability of the wireless channels, the queuing at the network nodes, and the frame-based decoding and playback of the video content at the receiver at very short time scales. To address the problem, we propose a low-complexity model-based rate adaptation algorithm for scalable video streaming systems, building on a novel performance model based on stochastic network calculus. We validate the analytic model using extensive simulations. We show that it allows fast near-optimal rate adaptation for fixed transmission paths, as well as cross-layer optimized routing and video rate adaptation in mesh networks, with less than 10% quality degradation compared to the best achievable performance.
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- Andersson, G., et al.
(författare)
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Cyber-security of SCADA systems
- 2012
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Ingår i: 2012 IEEE PES Innovative Smart Grid Technologies, ISGT 2012. - : IEEE. - 9781457721588 ; , s. 6175543-
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Konferensbidrag (refereegranskat)abstract
- After a general introduction of the VIKING EU FP7 project two specific cyber-attack mechanisms, which have been analyzed in the VIKING project, will be discussed in more detail. Firstly an attack and its consequences on the Automatic Generation Control (AGC) in a power system are investigated, and secondly the cyber security of State Estimators in SCADA systems is scrutinized.
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- Araldo, Andrea, et al.
(författare)
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Caching Encrypted Content Via Stochastic Cache Partitioning
- 2018
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Ingår i: IEEE/ACM Transactions on Networking. - : IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC. - 1063-6692 .- 1558-2566. ; 26:1, s. 548-561
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Tidskriftsartikel (refereegranskat)abstract
- In-network caching is an appealing solution to cope with the increasing bandwidth demand of video, audio, and data transfer over the Internet. Nonetheless, in order to protect consumer privacy and their own business, content providers (CPs) increasingly deliver encrypted content, thereby preventing Internet service providers (ISPs) from employing traditional caching strategies, which require the knowledge of the objects being transmitted. To overcome this emerging tussle between security and efficiency, in this paper we propose an architecture in which the ISP partitions the cache space into slices, assigns each slice to a different CP, and lets the CPs remotely manage their slices. This architecture enables transparent caching of encrypted content and can be deployed in the very edge of the ISP's network (i.e., base stations and femtocells), while allowing CPs to maintain exclusive control over their content. We propose an algorithm, called SDCP, for partitioning the cache storage into slices so as to maximize the bandwidth savings provided by the cache. A distinctive feature of our algorithm is that ISPs only need to measure the aggregated miss rates of each CP, but they need not know the individual objects that are requested. We prove that the SDCP algorithm converges to a partitioning that is close to the optimal, and we bound its optimality gap. We use simulations to evaluate SDCP's convergence rate under stationary and nonstationary content popularity. Finally, we show that SDCP significantly outperforms traditional reactive caching techniques, considering both CPs with perfect and with imperfect knowledge of their content popularity.
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- Araldo, Andrea, et al.
(författare)
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Stochastic Dynamic Cache Partitioning for Encrypted Content Delivery
- 2016
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Ingår i: Proceedings of the 28th International Teletraffic Congress, ITC 2016. - : IEEE Press. - 9780988304512 ; , s. 139-147
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Konferensbidrag (refereegranskat)abstract
- In-network caching is an appealing solution to cope with the increasing bandwidth demand of video, audio and data transfer over the Internet. Nonetheless, an increasing share of content delivery services adopt encryption through HTTPS, which is not compatible with traditional ISP-managed approaches like transparent and proxy caching. This raises the need for solutions involving both Internet Service Providers (ISP) and Content Providers (CP): by design, the solution should preserve business-critical CP information (e.g., content popularity, user preferences) on the one hand, while allowing for a deeper integration of caches in the ISP architecture (e.g., in 5G femto-cells) on the other hand. In this paper we address this issue by considering a content-oblivious ISP-operated cache. The ISP allocates the cache storage to various content providers so as to maximize the bandwidth savings provided by the cache: the main novelty lies in the fact that, to protect business-critical information, ISPs only need to measure the aggregated miss rates of the individual CPs and do not need to be aware of the objects that are requested, as in classic caching. We propose a cache allocation algorithm based on a perturbed stochastic subgradient method, and prove that the algorithm converges close to the allocation that maximizes the overall cache hit rate. We use extensive simulations to validate the algorithm and to assess its convergence rate under stationary and non-stationary content popularity. Our results (i) testify the feasibility of content-oblivious caches and (ii) show that the proposed algorithm can achieve within 10% from the global optimum in our evaluation.
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