المؤلفون: Chen, Zhengchuan, Lang, Kang, Pappas, Nikolaos, Yang, Howard H., Wang, Min, Tian, Zhong, Quek, Tony Q. S.

مصطلحات موضوعية: Computer Science - Information Theory, Electrical Engineering and Systems Science - Signal Processing

الوصف: Timely status updating is the premise of emerging interaction-based applications in the Internet of Things (IoT). Using redundant devices to update the status of interest is a promising method to improve the timeliness of information. However, parallel status updating leads to out-of-order arrivals at the monitor, significantly challenging timeliness analysis. This work studies the Age of Information (AoI) of a multi-queue status update system where multiple devices monitor the same physical process. Specifically, two systems are considered: the Basic System, which only has type-1 devices that are ad hoc devices located close to the source, and the Hybrid System, which contains additional type-2 devices that are infrastructure-based devices located in fixed points compared to the Basic System. Using the Stochastic Hybrid Systems (SHS) framework, a mathematical model that combines discrete and continuous dynamics, we derive the expressions of the average AoI of the considered two systems in closed form. Numerical results verify the accuracy of the analysis. It is shown that when the number and parameters of the type-1 devices/type-2 devices are fixed, the logarithm of average AoI will linearly decrease with the logarithm of the total arrival rate of type-2 devices or that of the number of type-1 devices under specific condition. It has also been demonstrated that the proposed systems can significantly outperform the FCFS M/M/N status update system.

الوصول الحر: http://arxiv.org/abs/2405.16965Test

المؤلفون: Ma, Kun, Feng, Chenyuan, Geraci, Giovanni, Yang, Howard H.

مصطلحات موضوعية: Computer Science - Information Theory, Electrical Engineering and Systems Science - Signal Processing

الوصف: In this paper, we introduce a novel mathematical framework for assessing the performance of joint communication and sensing (JCAS) in wireless networks, employing stochastic geometry as an analytical tool. We focus on deriving the meta distribution of the signal-to-interference ratio (SIR) for JCAS networks. This approach enables a fine-grained quantification of individual user or radar performance intrinsic to these networks. Our work involves the modeling of JCAS networks and the derivation of mathematical expressions for the JCAS SIR meta distribution. Through simulations, we validate both our theoretical analysis and illustrate how the JCAS SIR meta distribution varies with the network deployment density.

الوصول الحر: http://arxiv.org/abs/2404.01672Test

المؤلفون: Wang, Chenhao, Chen, Zihan, Pappas, Nikolaos, Yang, Howard H., Quek, Tony Q. S., Poor, H. Vincent

مصطلحات موضوعية: Computer Science - Machine Learning, Computer Science - Information Theory, Computer Science - Networking and Internet Architecture

الوصف: We propose a federated version of adaptive gradient methods, particularly AdaGrad and Adam, within the framework of over-the-air model training. This approach capitalizes on the inherent superposition property of wireless channels, facilitating fast and scalable parameter aggregation. Meanwhile, it enhances the robustness of the model training process by dynamically adjusting the stepsize in accordance with the global gradient update. We derive the convergence rate of the training algorithms, encompassing the effects of channel fading and interference, for a broad spectrum of nonconvex loss functions. Our analysis shows that the AdaGrad-based algorithm converges to a stationary point at the rate of $\mathcal{O}( \ln{(T)} /{ T^{ 1 - \frac{1}{\alpha} } } )$, where $\alpha$ represents the tail index of the electromagnetic interference. This result indicates that the level of heavy-tailedness in interference distribution plays a crucial role in the training efficiency: the heavier the tail, the slower the algorithm converges. In contrast, an Adam-like algorithm converges at the $\mathcal{O}( 1/T )$ rate, demonstrating its advantage in expediting the model training process. We conduct extensive experiments that corroborate our theoretical findings and affirm the practical efficacy of our proposed federated adaptive gradient methods.

الوصول الحر: http://arxiv.org/abs/2403.06528Test

المؤلفون: Zhu, Jiaqi, Pappas, Nikolaos, Yang, Howard H.

مصطلحات موضوعية: Computer Science - Information Theory

الوصف: We leverage the Multiplicative Weight Update (MWU) method to develop a decentralized algorithm that significantly improves the performance of dynamic time division duplexing (D-TDD) in small cell networks. The proposed algorithm adaptively adjusts the time portion allocated to uplink (UL) and downlink (DL) transmissions at every node during each scheduled time slot, aligning the packet transmissions toward the most appropriate link directions according to the feedback of signal-to-interference ratio information. Our simulation results reveal that compared to the (conventional) fixed configuration of UL/DL transmission probabilities in D-TDD, incorporating MWU into D-TDD brings about a two-fold improvement of mean packet throughput in the DL and a three-fold improvement of the same performance metric in the UL, resulting in the D-TDD even outperforming Static-TDD in the UL. It also shows that the proposed scheme maintains a consistent performance gain in the presence of an ascending traffic load, validating its effectiveness in boosting the network performance. This work also demonstrates an approach that accounts for algorithmic considerations at the forefront when solving stochastic problems.

الوصول الحر: http://arxiv.org/abs/2402.05641Test

المؤلفون: Hu, Xinyi, Pappas, Nikolaos, Yang, Howard H.

مصطلحات موضوعية: Computer Science - Machine Learning, Computer Science - Distributed, Parallel, and Cluster Computing

الوصف: Federated Learning (FL) has emerged as a privacy-preserving machine learning paradigm facilitating collaborative training across multiple clients without sharing local data. Despite advancements in edge device capabilities, communication bottlenecks present challenges in aggregating a large number of clients; only a portion of the clients can update their parameters upon each global aggregation. This phenomenon introduces the critical challenge of stragglers in FL and the profound impact of client scheduling policies on global model convergence and stability. Existing scheduling strategies address staleness but predominantly focus on either timeliness or content. Motivated by this, we introduce the novel concept of Version Age of Information (VAoI) to FL. Unlike traditional Age of Information metrics, VAoI considers both timeliness and content staleness. Each client's version age is updated discretely, indicating the freshness of information. VAoI is incorporated into the client scheduling policy to minimize the average VAoI, mitigating the impact of outdated local updates and enhancing the stability of FL systems.
Comment: 5 pages, 4 figures, ICASSP 2024

الوصول الحر: http://arxiv.org/abs/2402.05407Test

المؤلفون: Chen, Zihan, Yang, Howard H., Quek, Tony Q. S., Chong, Kai Fong Ernest

مصطلحات موضوعية: Computer Science - Machine Learning, Computer Science - Networking and Internet Architecture

الوصف: Personalized federated learning (PFL) has been widely investigated to address the challenge of data heterogeneity, especially when a single generic model is inadequate in satisfying the diverse performance requirements of local clients simultaneously. Existing PFL methods are inherently based on the idea that the relations between the generic global and personalized local models are captured by the similarity of model weights. Such a similarity is primarily based on either partitioning the model architecture into generic versus personalized components, or modeling client relationships via model weights. To better capture similar (yet distinct) generic versus personalized model representations, we propose \textit{spectral distillation}, a novel distillation method based on model spectrum information. Building upon spectral distillation, we also introduce a co-distillation framework that establishes a two-way bridge between generic and personalized model training. Moreover, to utilize the local idle time in conventional PFL, we propose a wait-free local training protocol. Through extensive experiments on multiple datasets over diverse heterogeneous data settings, we demonstrate the outperformance and efficacy of our proposed spectral co-distillation method, as well as our wait-free training protocol.
Comment: 13 pages, NeurIPS 2023. Code at https://github.com/jimmyc96/spectral-dis-FLTest

الوصول الحر: http://arxiv.org/abs/2401.17124Test

المؤلفون: Zhao, Fangming, Pappas, Nikolaos, Ma, Chuan, Sun, Xinghua, Quek, Tony Q. S., Yang, Howard H.

مصطلحات موضوعية: Computer Science - Networking and Internet Architecture

الوصف: We optimize the Age of Information (AoI) in mobile networks using the age-threshold slotted ALOHA (TSA) protocol. The network comprises multiple source-destination pairs, where each source sends a sequence of status update packets to its destination over a shared spectrum. The TSA protocol stipulates that a source node must remain silent until its AoI reaches a predefined threshold, after which the node accesses the radio channel with a certain probability. Using stochastic geometry tools, we derive analytical expressions for the transmission success probability, mean peak AoI, and time-average AoI. Subsequently, we obtain closed-form expressions for the optimal update rate and age threshold that minimize the mean peak and time-average AoI, respectively. In addition, we establish a scaling law for the mean peak AoI and time-average AoI in mobile networks, revealing that the optimal mean peak AoI and time-average AoI increase linearly with the deployment density. Notably, the growth rate of time-average AoI under TSA is half of that under conventional slotted ALOHA. When considering the optimal mean peak AoI, the TSA protocol exhibits comparable performance to the traditional slotted ALOHA protocol. These findings conclusively affirm the advantage of TSA in reducing higher-order AoI, particularly in densely deployed networks.
Comment: 21 pages. Update version after peer review

الوصول الحر: http://arxiv.org/abs/2312.10888Test

المؤلفون: Khan, Muhammad Azeem, Yang, Howard H., Chen, Zihan, Iera, Antonio, Pappas, Nikolaos

مصطلحات موضوعية: Computer Science - Networking and Internet Architecture, Computer Science - Machine Learning

الوصف: Data possesses significant value as it fuels advancements in AI. However, protecting the privacy of the data generated by end-user devices has become crucial. Federated Learning (FL) offers a solution by preserving data privacy during training. FL brings the model directly to User Equipments (UEs) for local training by an access point (AP). The AP periodically aggregates trained parameters from UEs, enhancing the model and sending it back to them. However, due to communication constraints, only a subset of UEs can update parameters during each global aggregation. Consequently, developing innovative scheduling algorithms is vital to enable complete FL implementation and enhance FL convergence. In this paper, we present a scheduling policy combining Age of Update (AoU) concepts and data Shapley metrics. This policy considers the freshness and value of received parameter updates from individual data sources and real-time channel conditions to enhance FL's operational efficiency. The proposed algorithm is simple, and its effectiveness is demonstrated through simulations.
Comment: IEEE Conference on Standards for Communications and Networking 2023

الوصول الحر: http://arxiv.org/abs/2312.10512Test

المؤلفون: Xu, Chao, Zhang, Xinyan, Yang, Howard H., Wang, Xijun, Pappas, Nikolaos, Niyato, Dusit, Quek, Tony Q. S.

مصطلحات موضوعية: Computer Science - Networking and Internet Architecture, Electrical Engineering and Systems Science - Signal Processing

الوصف: Many real-time applications of the Internet of Things (IoT) need to deal with correlated information generated by multiple sensors. The design of efficient status update strategies that minimize the Age of Correlated Information (AoCI) is a key factor. In this paper, we consider an IoT network consisting of sensors equipped with the energy harvesting (EH) capability. We optimize the average AoCI at the data fusion center (DFC) by appropriately managing the energy harvested by sensors, whose true battery states are unobservable during the decision-making process. Particularly, we first formulate the dynamic status update procedure as a partially observable Markov decision process (POMDP), where the environmental dynamics are unknown to the DFC. In order to address the challenges arising from the causality of energy usage, unknown environmental dynamics, unobservability of sensors'true battery states, and large-scale discrete action space, we devise a deep reinforcement learning (DRL)-based dynamic status update algorithm. The algorithm leverages the advantages of the soft actor-critic and long short-term memory techniques. Meanwhile, it incorporates our proposed action decomposition and mapping mechanism. Extensive simulations are conducted to validate the effectiveness of our proposed algorithm by comparing it with available DRL algorithms for POMDPs.

الوصول الحر: http://arxiv.org/abs/2310.19216Test

المؤلفون: Chen, Xiang, Guo, Zhiheng, Wang, Xijun, Yang, Howard H., Feng, Chenyuan, Su, Junshen, Zheng, Sihui, Quek, Tony Q. S.

مصطلحات موضوعية: Computer Science - Information Theory, Computer Science - Distributed, Parallel, and Cluster Computing, Computer Science - Machine Learning, Computer Science - Networking and Internet Architecture, Electrical Engineering and Systems Science - Signal Processing

الوصف: Future wireless communication networks are in a position to move beyond data-centric, device-oriented connectivity and offer intelligent, immersive experiences based on task-oriented connections, especially in the context of the thriving development of pre-trained foundation models (PFM) and the evolving vision of 6G native artificial intelligence (AI). Therefore, redefining modes of collaboration between devices and servers and constructing native intelligence libraries become critically important in 6G. In this paper, we analyze the challenges of achieving 6G native AI from the perspectives of data, intelligence, and networks. Then, we propose a 6G native AI framework based on foundation models, provide a customization approach for intent-aware PFM, present a construction of a task-oriented AI toolkit, and outline a novel cloud-edge-end collaboration paradigm. As a practical use case, we apply this framework for orchestration, achieving the maximum sum rate within a wireless communication system, and presenting preliminary evaluation results. Finally, we outline research directions for achieving native AI in 6G.
Comment: 8 pages, 4 figures, 1 table

الوصول الحر: http://arxiv.org/abs/2310.17471Test