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Wireless Federated Learning With Hybrid Local and Centralized Training: A Latency Minimization design

IEEE Journal on Selected Topics in Signal Processing 2023年第1期17卷 248-263页

作者： Huang, Ning Dai, Minghui Wu, Yuan Quek, Tony Q. S. Shen, Xuemin State Key Lab of Internet of Things for Smart City China University of Macau Department of Computer and Information Science China University of Macau State Key Laboratory of Internet of Things for Smart City The Department of Computer and Information Science China Zhuhai Um Science and Technology Research Institute Zhuhai519031 China Singapore University of Technology and Design Information Systems Technology and Design Pillar 487372 Singapore National Cheng Kung University Taiwan University of Waterloo Department of Electrical and Computer Engineering WaterlooONN2L 3G1 Canada

Wireless federated learning (FL) is a collaborative machine learning (ML) framework in which wireless client-devices independently train their ML models and send the locally trained models to the FL server for aggregation. In this paper, we consider the coexistence of privacy-sensitive client-devices and privacy-insensitive yet computing-resource constrained client-devices, and propose an FL framework with a hybrid centralized training and local training. Specifically, the privacy-sensitive client-devices perform local ML model training and send their local models to the FL server. Each privacy-insensitive client-device can have two options, i.e., (i) conducting a local training and then sending its local model to the FL server, and (ii) directly sending its local data to the FL server for the centralized training. The FL server, after collecting the data from the privacy-insensitive client-devices (which choose to upload the local data), conducts a centralized training with the received datasets. The global model is then generated by aggregating (i) the local models uploaded by the client-devices and (ii) the model trained by the FL server centrally. Focusing on this hybrid FL framework, we firstly analyze its convergence feature with respect to the client-devices' selections of local training or centralized training. We then formulate a joint optimization of client-devices' selections of the local training or centralized training, the FL training configuration (i.e., the number of the local iterations and the number of the global iterations), and the bandwidth allocations to the client-devices, with the objective of minimizing the overall latency for reaching the FL convergence. Despite the non-convexity of the joint optimization problem, we identify its layered structure and propose an efficient algorithm to solve it. Numerical results demonstrate the advantage of our proposed FL framework with the hybrid local and centralized training as well as our proposed alg

关键词： Information management

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Deep Generative Model and Its Applications in Efficient Wireless Network Management: A Tutorial and Case Study

arXiv

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arXiv 2023年

作者： Liu, Yinqiu Du, Hongyang Niyato, Dusit Kang, Jiawen Xiong, Zehui Kim, Dong In Jamalipour, Abbas The School of Computer Science and Engineering Nanyang Technological University Singapore The School of Automation Guangdong University of Technology China The Pillar of Information Systems Technology and Design Singapore University of Technology and Design Singapore The Department of Electrical and Computer Engineering Sungkyunkwan University Korea Republic of The School of Electrical and Information Engineering University of Sydney Australia

With the phenomenal success of diffusion models and ChatGPT, deep generation models (DGMs) have been experiencing explosive growth from 2022. Not limited to content generation, DGMs are also widely adopted in Internet of Things, Metaverse, and digital twin, due to their outstanding ability to represent complex patterns and generate plausible samples. In this article, we explore the applications of DGMs in a crucial task, i.e., improving the efficiency of wireless network management. Specifically, we firstly overview the generative AI, as well as three representative DGMs. Then, a DGM-empowered framework for wireless network management is proposed, in which we elaborate the issues of the conventional network management approaches, why DGMs can address them efficiently, and the step-by-step workflow for applying DGMs in managing wireless networks. Moreover, we conduct a case study on network economics, using the state-of-the-art DGM model, i.e., diffusion model, to generate effective contracts for incentivizing the mobile AI-Generated Content (AIGC) services. Last but not least, we discuss important open directions for the further research. © 2023, CC BY.

关键词： Wireless networks

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Intelligent upper-limb exoskeleton integrated with soft bioelectronics and deep learning for intention-driven augmentation

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npj Flexible Electronics 2024年第1期8卷 856-868页

作者： Jinwoo Lee Kangkyu Kwon Ira Soltis Jared Matthews Yoon Jae Lee Hojoong Kim Lissette Romero Nathan Zavanelli Youngjin Kwon Shinjae Kwon Jimin Lee Yewon Na Sung Hoon Lee Ki Jun Yu Minoru Shinohara Frank L.Hammond Woon-Hong Yeo Department of Mechanical Roboticsand Energy EngineeringDongguk UniversitySeoul 04620Republic of Korea IEN Center for Wearable Intelligent Systems and Healthcare Institute for Electronics and NanotechnologyGeorgia Institute of TechnologyAtlantaGA 30332USA School of Electrical and Computer Engineering Georgia Institute of TechnologyAtlantaGA 30332USA George W.Woodruff School of Mechanical Engineering Georgia Institute of TechnologyAtlantaGA 30332USA School of Industrial Design Georgia Institute of TechnologyAtlantaGA 30332USA School of Electrical and Electronic Engineering Yonsei UniversitySeoul 03722Republic of Korea School of Biological Sciences Georgia Institute of TechnologyAtlantaGA 30332USA Wallace H.Coulter Department of Biomedical Engineering Georgia Institute of Technology and Emory University School of MedicineAtlantaGA 30332USA Institute for Materials Parker H.Petit Institute for Bioengineering and BiosciencesInstitute for Robotics and Intelligent MachinesGeorgia Institute of TechnologyAtlantaGA 30332USA

The age and stroke-associated decline in musculoskeletal strength degrades the ability to perform daily human tasks using the upper ***,we introduce an intelligent upper-limb exoskeleton system that utilizes deep learning to predict human intention for strength *** embedded soft wearable sensors provide sensory feedback by collecting real-time muscle activities,which are simultaneously computed to determine the user’s intended *** deep learning predicts four upper-limb joint motions with an average accuracy of 96.2%at a 500–550 ms response rate,suggesting that the exoskeleton operates just by human *** addition,an array of soft pneumatics assists the intended movements by providing 897 newtons of force while generating a displacement of 87mm at *** intent-driven exoskeleton can reduce human muscle activities by 3.7 times on average compared to the unassisted exoskeleton.

关键词： skeleton learning utilize

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Performance Analysis of Free-Space Information Sharing in Full-Duplex Semantic Communications

Performance Analysis of Free-Space Information Sharing in Fu...

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IEEE Conference on Global Communications (GLOBECOM)

作者： Hongyang Du Jiacheng Wang Dusit Niyato Jiawen Kang Zehui Xiong Dong In Kim Boon Hee Soong School of Computer Science and Engineering Nanyang Technological University Singapore School of Automation Guangdong University of Technology China Pillar of Information Systems Technology and Design Singapore University of Technology and Design Singapore Department of Electrical and Computer Engineering Sungkyunkwan University South Korea Department of Electrical and Electronic Engineering Nanyang Technological University Singapore

In next-generation Internet services, such as Metaverse, the mixed reality (MR) technique plays a vital role. Yet the limited computing capacity of the user-side MR headset-mounted device (HMD) prevents its further application, especially in scenarios that require a lot of computation. One way out of this dilemma is to design an efficient information sharing scheme among users to replace the heavy and repetitive computation. In this paper, we propose a free-space information sharing mechanism based on full-duplex device-to-device (D2D) semantic communications. Specifically, the view images of MR users in the same real-world scenario may be analogous. Therefore, when one user (i.e., a device) completes some computation tasks, the user can send his own calculation results and the semantic features extracted from the user's own view image to nearby users (i.e., other devices). On this basis, other users can use the received semantic features to obtain the spatial matching of the computational results under their own view images without repeating the computation. Using generalized small-scale fading models, we analyze the key performance indicators of full-duplex D2D communications, including channel capacity and bit error probability, which directly affect the transmission of semantic information. Finally, the numerical analysis experiment proves the effectiveness of our proposed methods.

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Efficient On-device Transfer Learning using Activation Memory Reduction

Efficient On-device Transfer Learning using Activation Memor...

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International Conference on Fog and Mobile Edge Computing (FMEC)

作者： Amin Yoosefi Hamid Mousavi Masoud Daneshtalab Mehdi Kargahi Innovation Design and Engineering Mä lardalen University Västerås Sweden School of Electrical and Computer Engineering University of Tehran Tehran Iran School of Computer Systems Tallinn University of Technology Tallinn Estonia

On-device transfer learning suggests fine-tuning pretrained neural networks on new input data directly on edge devices. The memory limitation of edge devices necessitates using memory-efficient fine-tuning methods. Fine-tuning involves two primary phases: the forward-pass phase and the backward-pass phase. The forward-pass phase generates output activations, and the backward-pass phase computes gradients and updates the parameters accordingly. Although the forward-pass phase demands a temporary memory to store a layer's input and output activations, the backward-pass phase may require storing the output activations from all layers to compute gradients. This fact introduces the memory cost of the backward-pass phase as the main contributor to the huge training memory demands of deep neural networks (DNNs), which has been the focus of many studies. However, little attention has been made to how the temporary activation memory involved in the forward-pass phase may also act as the memory bottleneck, which is the main focus of this paper. This paper aims to mitigate this memory bottleneck by pruning unimportant channels from layers that require significant temporary activation memory. Experimental results demonstrate how the proposed method effectively reduces peak activation memory and total memory costs of MobileNetV2 by 65% and 59%, respectively, at the cost of 3% accuracy drop.

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Streamlined Transmission: A Semantic-Aware XR Deployment Framework Enhanced by Generative AI

arXiv

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arXiv 2024年

作者： Yang, Wanting Xiong, Zehui Quek, Tony Q.S. Shen, Xuemin The Pillar of Information Systems Technology and Design Singapore University of Technology and Design Singapore Singapore The Department of Electrical and Computer Engineering University of Waterloo WaterlooONN2L 3G1 Canada

In the era of 6G, featuring compelling visions of digital twins and metaverses, Extended Reality (XR) has emerged as a vital conduit connecting the digital and physical realms, garnering widespread interest. Ensuring a fully immersive wireless XR experience stands as a paramount technical necessity, demanding the liberation of XR from the confines of wired connections. In this paper, we first introduce the technologies applied in the wireless XR domain, delve into their benefits and limitations, and highlight the ongoing challenges. We then propose a novel deployment framework for a broad XR pipeline, termed "GeSa-XRF", inspired by the core philosophy of Semantic Communication (SemCom) which shifts the concern from "how" to transmit to "what" to transmit. Particularly, the framework comprises three stages: data collection, data analysis, and data delivery. In each stage, we integrate semantic awareness to achieve streamlined transmission and employ Generative Artificial Intelligence (GAI) to achieve collaborative refinements. For the data collection of multi-modal data with differentiated data volumes and heterogeneous latency requirements, we propose a novel SemCom paradigm based on multi-modal fusion and separation and a GAI-based robust superposition scheme. To perform a comprehensive data analysis, we employ multitask learning to perform the prediction of field of view and personalized attention and discuss the possible preprocessing approaches assisted by GAI. Lastly, for the data delivery stage, we present a semantic-aware multicast-based delivery strategy aimed at reducing pixel level redundant transmissions and introduce the GAI collaborative refinement approach. The performance gain of the proposed GeSa-XRF is preliminarily demonstrated through a case study. Copyright © 2024, The Authors. All rights reserved.

关键词： Artificial intelligence

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Invertibility of Discrete-Time Linear systems with Sparse Inputs

arXiv

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arXiv 2024年

作者： Poe, Kyle Mallada, Enrique Vidal, René Applied Mathematics and Computational Science Group University of Pennsylvania PA19104 United States Department of Electrical and Computer Engineering Johns Hopkins University MD21218 United States Departments of Electrical and Systems Engineering Radiology Computer and Information Science Statistics and Data Science University of Pennsylvania PA19104 United States

One of the fundamental problems of interest for discrete-time linear systems is whether its input sequence may be recovered given its output sequence, a.k.a. the left inversion problem. Many conditions on the state space geometry, dynamics, and spectral structure of a system have been used to characterize the well-posedness of this problem, without assumptions on the inputs. However, certain structural assumptions, such as input sparsity, have been shown to translate to practical gains in the performance of inversion algorithms, surpassing classical guarantees. Establishing necessary and sufficient conditions for left invertibility of systems with sparse inputs is therefore a crucial step toward understanding the performance limits of system inversion under structured input assumptions. In this work, we provide the first necessary and sufficient characterizations of left invertibility for linear systems with sparse inputs, echoing classic characterizations for standard linear systems. The key insight in deriving these results is in establishing the existence of two novel geometric invariants unique to the sparse-input setting, the weakly unobservable and strongly reachable subspace arrangements. By means of a concrete example, we demonstrate the utility of these characterizations. We conclude by discussing extensions and applications of this framework to several related problems in sparse control. © 2024, CC BY.

关键词： Linear systems

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Exploring Equilibrium Strategies in Network Games with Generative AI

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IEEE Network 2024年

作者： Yang, Yaoqi Du, Hongyang Sun, Geng Xiong, Zehui Niyato, Dusit Han, Zhu Army Engineering University of PLA College of Communications Engineering Nanjing210000 China University of Hong Kong Department of Electrical and Electronic Engineering Pok Fu Lam Hong Kong Jilin University College of Computer Science and Technology Changchun130012 China Nanyang Technological University College of Computing and Data Science 639798 Singapore Singapore University of Technology and Design Information Systems Technology and Design Pillar Singapore College of Computing and Data Science NTU Singapore University of Houston Department of Electrical and Computer Engineering HoustonTX77004 United States

Game theory offers a powerful framework for analyzing strategic interactions among decision-makers, providing tools to model, analyze, and predict their behavior. However, implementing game theory can be challenging due to difficulties in deriving solutions, understanding interactions, and ensuring optimal performance. Traditional non-AI and discriminative AI approaches have made valuable contributions but struggle with limitations in handling large-scale games and dynamic scenarios. In this context, generative AI emerges as a promising solution because of its superior data analysis and generation capabilities. This paper comprehensively summarizes the challenges, solutions, and outlooks of combining generative AI with game theory. We start with reviewing the limitations of traditional non-AI and discriminative AI approaches in employing game theory, and then highlight the necessity and advantages of integrating generative AI. Next, we explore the applications of generative AI in various stages of the game theory lifecycle, including model formulation, solution derivation, and strategy improvement. Additionally, from game theory viewpoint, we propose a generative AI-enabled framework for optimizing machine learning model performance against false data injection attacks, supported by a case study to demonstrate its effectiveness. Finally, we outline future research directions for generative AI-enabled game theory, paving the way for its further advancements and development. © 1986-2012 IEEE.

关键词： Generative adversarial networks

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To Lead or to Follow? Adaptive Robot Task Planning in Human-Robot Collaboration

arXiv

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arXiv 2024年

作者： Noormohammadi-Asl, Ali Smith, Stephen L. Dautenhahn, Kerstin Department of Electrical and Computer Engineering Faculty of Engineering University of Waterloo WaterlooONN2L 3G1 Canada Department of Systems Design Engineering Faculty of Engineering University of Waterloo WaterlooONN2L 3G1 Canada

Adaptive task planning is fundamental to ensuring effective and seamless human-robot collaboration. This paper introduces a robot task planning framework that takes into account both human leading/following preferences and performance, specifically focusing on task allocation and scheduling in collaborative settings. We present a proactive task allocation approach with three primary objectives: enhancing team performance, incorporating human preferences, and upholding a positive human perception of the robot and the collaborative experience. Through a user study, involving an autonomous mobile manipulator robot working alongside participants in a collaborative scenario, we confirm that the task planning framework successfully attains all three intended goals, thereby contributing to the advancement of adaptive task planning in human-robot collaboration. This paper mainly focuses on the first two objectives, and we discuss the third objective, participants’ perception of the robot, tasks, and collaboration in a companion paper. © 2024, CC BY-NC-ND.

关键词： Manipulators

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AI-Generated Incentive Mechanism and Full-Duplex Semantic Communications for Information Sharing

TechRxiv

引用

TechRxiv 2023年

作者： Du, Hongyang Wang, Jiacheng Niyato, Dusit Kang, Jiawen Xiong, Zehui In Kim, Dong The School of Computer Science and Engineering Nanyang Technological University Singapore The School of Automation Guangdong University of Technology China The Pillar of Information Systems Technology and Design Singapore University of Technology and Design Singapore The Department of Electrical and Computer Engineering Sungkyunkwan University Korea Republic of

The next generation of Internet services, such as Metaverse, rely on mixed reality (MR) technology to provide immersive user experiences. However, limited computation power of MR headset-mounted devices (HMDs) hinders the deployment of such services. Therefore, we propose an efficient information-sharing scheme based on full-duplex device-to-device (D2D) semantic communications to address this issue. Our approach enables users to avoid heavy and repetitive computational tasks, such as artificial intelligence-generated content (AIGC) in the view images of all MR users. Specifically, a user can transmit the generated content and semantic information extracted from their view image to nearby users, who can then use this information to obtain the spatial matching of computation results under their view images. We analyze the performance of full-duplex D2D communications, including the achievable rate and bit error probability, by using generalized small-scale fading models. To facilitate semantic information sharing among users, we design a contract theoretic AI-generated incentive mechanism. The proposed diffusion model generates the optimal contract design, outperforming two deep reinforcement learning algorithms, i.e., proximal policy optimization and soft actor-critic algorithms. Our numerical analysis experiment proves the effectiveness of our proposed methods. © 2023, CC BY.

关键词： Reinforcement learning

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