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3rd IEEE International Mediterranean Conference on Communications and Networking, MeditCom 2023

作者： Wang, Jiali Mao, Yijie Wang, Ting Shi, Yuanming Shanghai Key Lab. of Trustworthy Computing East China Normal University MoE Engineering Research Center of Software/Hardware Co-Design Technology and Application China China

ISBN: (纸本)9798350333732

In this paper, we investigate a green federated learning (FL) framework over cloud radio access network (Cloud-RAN) system that comprises a server, multiple devices and remote radio heads (RRHs). Each device utilizes quantized neural networks (QNNs) and sends quantized model parameters to the server to save energy consumption via RRHs. The server aggregates all the signals to update the global model parameters and broadcasts the updated parameters to the selected devices. In this context, we propose an energy consumption model for the QNN training and communication model over Cloud-RAN. We develop an energy minimization problem based on the proposed energy model. We jointly design fronthaul rate allocation, device transmit power, and precision level of QNNs while ensuring target accuracy, transmit power budget and limited fronthaul capacity. Guided by the convergence analysis, we adopt alternative optimization method to solve the energy minimization problem. The simulation outcomes demonstrate that the FL framework suggested can considerably diminish energy usage in comparison to other traditional methods. This has immense potential in realizing a sustainable and eco-friendly FL over Cloud-RAN. © 2023 IEEE.

关键词： Radio access networks

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Green Federated Learning over Cloud-RAN with Limited Fronthaul and Quantized Neural Networks

Green Federated Learning over Cloud-RAN with Limited Frontha...

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Communications and Networking (MeditCom), IEEE International Mediterranean Conference on

作者： Jiali Wang Yijie Mao Ting Wang Yuanming Shi MoE Engineering Research Center of Software/Hardware Co-Design Technology and Application Shanghai Key Lab. of Trustworthy Computing East China Normal University China School of Information Science and Technology (SIST) ShanghaiTech University China

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Towards Efficient Workflow Scheduling Over Yarn Cluster Using Deep Reinforcement Learning

Towards Efficient Workflow Scheduling Over Yarn Cluster Usin...

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IEEE Conference on Global Communications (GLOBECOM)

作者： Jianguo Xue Ting Wang Puyu Cai MoE Engineering Research Center of Software/Hardware Co-design Technology and Application Shanghai Key Lab. of Trustworthy Computing East China Normal University China Department of Computer Science and Engineering Michigan State University USA

Hadoop Yarn is an open-source cluster manager responsible for resource management and job scheduling. However, data-driven applications are typically organized into workflows that consist of a series of jobs with dependencies. Yarn does not manage users' workflows and only considers the current job rather than the entire workflow when scheduling. In practice, multiple workflows share the same Yarn cluster and are pre-assigned separate Yarn resource queues to avoid mutual interference. However, this coarse-grained resource division can sometimes result in low resource utilization and increased pending time of jobs on the Yarn queue. For instance, one resource queue may have exhausted its quota while still having pending jobs, while other queues may have availab.e resources but cannot begin executing any jobs due to unfulfilled data dependencies. To address this problem, we propose a deep reinforcement learning-based workflow scheduling scheme that takes into account job dependencies, job priorities, and dynamic resource usage. The proposed approach can intelligently identify and utilize free windows of different resource queues. Our simulation results demonstrate that the proposed DRL-based workflow scheduling scheme can significantly reduce the average job latency compared to existing approaches.

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Federated Linear Bandit Learning via Over-the-air Computation

arXiv

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arXiv 2023年

作者： Wang, Jiali Jiang, Yuning Liu, Xin Wang, Ting Shi, Yuanming MoE Engineering Research Center of Software/Hardware Co-design Technology and Application Shanghai Key Lab. of Trustworthy Computing East China Normal University China Automatic Control Laboratory EPFL Switzerland ShanghaiTech University China

In this paper, we investigate federated contextual linear bandit learning within a wireless system that comprises a server and multiple devices. Each device interacts with the environment, selects an action based on the received reward, and sends model updates to the server. The primary objective is to minimize cumulative regret across all devices within a finite time horizon. To reduce the communication overhead, devices communicate with the server via over-the-air computation (AirComp) over noisy fading channels, where the channel noise may distort the signals. In this context, we propose a customized federated linear bandits scheme, where each device transmits an analog signal, and the server receives a superposition of these signals distorted by channel noise. A rigorous mathematical analysis is conducted to determine the regret bound of the proposed scheme. Both theoretical analysis and numerical experiments demonstrate the competitive performance of our proposed scheme in terms of regret bounds in various settings. © 2023, CC BY-NC-SA.

关键词： Fading channels

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Satellite Federated Fine-Tuning for Foundation Models in Space Computing Power Networks

arXiv

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arXiv 2025年

作者： Zhu, Yan Zhu, Jingyang Wang, Ting Shi, Yuanming Jiang, Chunxiao Letaief, Khaled Ben MoE Engineering Research-Center of Software/Hardware Co-design Technology and Application The Shanghai Key Lab. of Trustworthy Computing East China Normal University Shanghai200062 China School of Information Science and Technology ShanghaiTech University Shanghai201210 China Beijing National Research Center for Information Science and Technology Tsinghua University Beijing100084 China Department of Electronic and Computer Engineering The Hong Kong University of Science and Technology Hong Kong

Advancements in artificial intelligence (AI) and low-earth orbit (LEO) satellites have promoted the application of large remote sensing foundation models for various downstream tasks. However, direct downloading of these models for fine-tuning on the ground is impeded by privacy concerns and limited bandwidth. Satellite federated learning (FL) offers a solution by enabling model fine-tuning directly on-board satellites and aggregating model updates without data downloading. Nevertheless, for large foundation models, the computational capacity of satellites is insufficient to support effective on-board fine-tuning in traditional satellite FL frameworks. To address these challenges, we propose a satellite-ground collab.rative federated fine-tuning framework. The key of the framework lies in how to reasonably decompose and allocate model components to alleviate insufficient on-board computation capabilities. During fine-tuning, satellites exchange intermediate results with ground stations or other satellites for forward propagation and back propagation, which brings communication challenges due to the special communication topology of space transmission networks, such as intermittent satellite-ground communication, short duration of satellite-ground communication windows, and unstable inter-orbit inter-satellite links (ISLs). To reduce transmission delays, we further introduce tailored communication strategies that integrate both communication and computing resources. Specifically, we propose a parallel intra-orbit communication strategy, a topology-aware satellite-ground communication strategy, and a latency-minimalization inter-orbit communication strategy to reduce space communication costs. Simulation results demonstrate significant reductions in training time with improvements of approximately 33%. Copyright © 2025, The Authors. All rights reserved.

关键词： Satellite communication systems

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Federated Linear Bandit Learning via Over-the-air Computation

Federated Linear Bandit Learning via Over-the-air Computatio...

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IEEE Conference on Global Communications (GLOBECOM)

作者： Jiali Wang Yuning Jiang Xin Liu Ting Wang Yuanming Shi MoE Engineering Research Center of Software/Hardware Co-design Technology and Application Shanghai Key Lab. of Trustworthy Computing East China Normal University China School of Information Science and Technology (SIST) ShanghaiTech University China Automatic Control Laboratory Ecole Polytechnique Fédérale de Lausanne (EPFL) Switzerland

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Federated Reinforcement Learning for Electric Vehicles Charging Control on Distribution Networks

arXiv

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arXiv 2023年

作者： Qian, Junkai Jiang, Yuning Liu, Xin Wang, Qiong Wang, Ting Shi, Yuanming Chen, Wei The MoE Engineering Research Center of Software/Hardware Co-design Technology and Application The Shanghai Key Lab. of Trustworthy Computing East China Normal University China The Automatic Control Laboratory EPFL Switzerland The School of Information Science and Technology ShanghaiTech University China The State Grid Beijing Electric Power Company China The Department of Electronic Engineering Tsinghua University China

With the growing popularity of electric vehicles (EVs), maintaining power grid stability has become a significant challenge. To address this issue, EV charging control strategies have been developed to manage the switch between vehicle-to-grid (V2G) and grid-to-vehicle (G2V) modes for EVs. In this context, multi-agent deep reinforcement learning (MADRL) has proven its effectiveness in EV charging control. However, existing MADRL-based approaches fail to consider the natural power flow of EV charging/discharging in the distribution network and ignore driver privacy. To deal with these problems, this paper proposes a novel approach that combines multi-EV charging/discharging with a radial distribution network (RDN) operating under optimal power flow (OPF) to distribute power flow in real time. A mathematical model is developed to describe the RDN load. The EV charging control problem is formulated as a Markov Decision Process (MDP) to find an optimal charging control strategy that balances V2G profits, RDN load, and driver anxiety. To effectively learn the optimal EV charging control strategy, a federated deep reinforcement learning algorithm named FedSAC is further proposed. Comprehensive simulation results demonstrate the effectiveness and superiority of our proposed algorithm in terms of the diversity of the charging control strategy, the power fluctuations on RDN, the convergence efficiency, and the generalization ability. © 2023, CC BY-NC-SA.

关键词： Reinforcement learning

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Multicore-based performance optimization for dense matrix computation

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2nd International Conference on Future Control and Automation, ICFCA 2012

作者： Mao, Guoyong Zhang, Xiaobin Li, Yun Li, Yujie Wei, Laizhi Department of Electronic Information and Electric Engineering Changzhou Institute of Technology Changzhou Key Lab. for Research and Application of Software Technology Changzhou 213002 China College of Information Engineering Yangzhou University Yangzhou 225009 China

ISBN: (纸本)9783642310027

To make the traditional applications benefit from multicore processors, the traditional Gaussian Elimination algorithm is improved to enhance its parallel performance under multicore architecture by matrix partition. The stability of the original algorithm is guaranteed. The hit rate of cache is improved by adjusting the computation sequence, the experiment shows that the speedup can reach 1.8 under duo core CPU environment when evaluating the inverse of dense matrix. © 2012 Springer-Verlag.

关键词： Matrix algebra

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STeC: A location-triggered specification language for real-time systems

STeC: A location-triggered specification language for real-t...

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作者： Chen, Yixiang MoE Engineering Research Center for Software/Hardware Co-design Technology and Application Shanghai Key Lab. for Trustworthy Computing East China Normal University Shanghai 200062 China

ISBN: (纸本)9780769546698

Internet of Things (IoT) or Cyber-Physical Systems (CPS) is a new trend of real-time systems in the area of information technology. This paper introduces a spatiotemporal consistence language for real-time systems (Shortly, STeC). The consistence requires that a process do its tasks at the required location or time. Thus, this language provides a location-triggered specification for real-time systems. The interaction between real-time agents deals with agent2agent communications. STeC looks like an extension of precess algebra CSP. But, the execution-time of actions and status of agents are stressed. Two kinds of interrupts time and interaction break are considered. Following the Dijkstra's guard style, nondeterministic choice phase guarded by communications is introduced. After setting up the syntax, its operational semantics is introduced. As an example, the railroad crossing problem is specified in terms of this language STeC. © 2012 IEEE.

关键词： Specification languages

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Partition-based optimizing algorithm for dense matrix computation under multicore environment

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Journal of Computational Information Systems 2010年第14期6卷 4923-4932页

作者： Mao, Guoyong Zhang, Xiaobin Li, Yun Li, Yujie Wei, Laizhi Department of Electronic Information and Electric Engineering Changzhou Institute of Technology Changzhou 213002 China Changzhou Key Lab. for Research and Application of Software Technology Changzhou 213002 China College of Information Engineering Yangzhou University Yangzhou 225009 China

With the development of multicore chips, it is of great need for people to study the optimization algorithm of matrix operation under multicore environment, so as to make full use of the CPU power;however, the existing parallel algorithm for matrix operation can hardly have the best of multicore chips. To make the traditional applications benefit from multicore processors, the traditional Gaussian Elimination algorithm is improved to enhance its parallel performance under multicore architecture by matrix partition. The stability of the original algorithm is guaranteed. The hit rate of cache is improved by adjusting the computation sequence, the experiment shows that the speedup can reach almost 3 under 4 core CPU environments when evaluating the inverse of dense matrix. © 2010 Binary Information Press.

关键词： Inverse problems

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