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Enhancing Federated Learning Generalization through Momentum Alignment in 6G networks

IEEE network 2025年

作者： Jiang, Lei Wang, Xiaoding Lin, Hui Fujian Normal University Fujian Provincial Key Laboratory of Network Security and Cryptology College of Computer and Cyber Security Fujian Fuzhou350117 China

As artificial intelligence (AI) technology continues to advance and the demand for AI applications grows, the development of sixth-generation (6G) wireless networks has become increasingly targeted. These networks are designed to be highly compatible with AI technologies, laying the foundation for revolutionary communication capabilities. In this context, federated learning, a technique that trains models across multiple distributed nodes without sharing raw data, becomes particularly significant. However, deploying federated learning in a 6G wireless network environment faces several challenges. Firstly, data heterogeneity may lead to decreased performance of the global model trained. Secondly, global models might converge to sharp loss landscape during distributed training, which weakens their generalizability. Some studies improve generalization by optimizing local models to converge to flatter loss landscape. However, this does not necessarily ensure that the aggregated global model also converges to a flatter landscape. To address these challenges, this paper introduces Federated Momentum Alignment(FedMA), a novel method designed to optimize the performance of client models in 6G networks by aligning momentum during model training. In the FedMA, the momentum alignment mechanism we propose not only prompts local models to reference future update directions during training to alleviate model drift caused by data heterogeneity, but also facilitates the convergence of global models to a flat loss landscape by aligning global objectives with local ones, thereby enhancing the model's generalization ability. Experimental results show that FedMA improves performance by 4.62% compared to the state-of-the-art federated learning methods. © 1986-2012 IEEE.

关键词： 6G networks Federated Learning Loss Landscape Model Generalization Momentum

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AH-TDMA: An Adaptive Heterogeneous Tridiagonal Matrix Algorithm on the New Sunway Supercomputer 29

AH-TDMA: An Adaptive Heterogeneous Tridiagonal Matrix Algori...

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29th IEEE International Conference on Parallel and Distributed Systems, ICPADS 2023

作者： Tian, Min Liu, Qi Li, Yanlong Du, Wei Ren, Zenghui Liu, Yue Qilu University of Technology Shandong Academy of Sciences Key Laboratory of Computing Power Network and Information Security Ministry of Education Shandong Computer Science Center National Supercomputer Center in Jinan China Shandong Fundamental Research Center for Computer Science Shandong Provincial Key Laboratory of Computer Networks China Jinan Institute of Supercomputing Technology Jinan Key Laboratory of High Performance Computing China

ISBN: (纸本)9798350330717

The solution of tridiagonal linear systems is used in in various fields and plays a crucial role in numerical simulations. However, there is few efficient solver for tridiagonal linear systems on the new Sunway supercomputer. Based on a three-dimensional heat conduction problem, we propose an adaptive heterogeneous tridiagonal matrix algorithm (AH-TDMA). Our major innovations include: (1) To address computational hotspots within AH-TDMA, a multi-level parallel approach involving MPI+Athread has been adopted. (2) An adaptive data partitioning scheme has been set up to achieve load balance. (3) Employing direct memory access and establishing shared space between registers and main memory, instead of employing discrete memory access, is done to enhance memory access efficiency. (4) The optimization of the loop structure has been made in adjusting the sequencing of the dual-layered loops to reduce communication overhead. The experimental results show that, with one core group, the AH-TDMA achieves a speedup of 99.6 times for hotspot and total time speedup up to 59.4 times compared to the Parallel and Scalable Library for Tridiagonal Matrix Algorithm (PaScal TDMA). The AH-TDMA is scalable up to 2048 core groups, with a parallel efficiency of 69.2%. © 2023 IEEE.

关键词： adaptive heterogeneous algorithm MPI+Athread Sunway supercomputer three-dimensional heat conduction problem tridiagonal linear systems

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An overview on IRS-enabled sensing and communications for 6G: architectures, fundamental limits, and joint beamforming designs

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Science China(information Sciences) 2025年第5期68卷 170-193页

作者： Xianxin SONG Yuan FANG Feng WANG Zixiang REN Xianghao YU Ye ZHANG Fan LIU Jie XU Derrick Wing Kwan NG Rui ZHANG Shuguang CUI School of Science and Engineering (SSE) Shenzhen Future Network of Intelligence Institute (FNii-Shenzhen)and Guangdong Provincial Key Laboratory of Future Networks of Intelligence The Chinese University of Hong Kong Department of Electrical Engineering City University of Hong Kong School of Information Engineering Guangdong University of Technology Key Laboratory of Wireless-Optical Communications Chinese Academy of Sciences School of Information Science and Technology University of Science and Technology of China Computer School Beijing Information Science and Technology University National Mobile Communications Research Laboratory School of Information Science and Engineering Southeast University School of Electrical Engineering and Telecommunications University of New South Wales School of Science and Engineering Shenzhen Research Institute of Big Data The Chinese University of Hong Kong Department of Electrical and Computer Engineering National University of Singapore

This study presents an overview on intelligent reflecting surface(IRS)-enabled sensing and communication for the forthcoming sixth-generation(6G) wireless networks, in which IRSs are strategically deployed to proactively reconfigure wireless environments to improve both sensing and communication(S&C) performance. First, we exploit a single IRS to enable wireless sensing in the base station's(BS's) non-line-of-sight(NLoS) area. In particular, we present three IRS-enabled NLoS target sensing architectures with fully-passive, semi-passive, and active IRSs, respectively. We compare their pros and cons by analyzing the fundamental sensing performance limits for target detection and parameter estimation. Next, we consider a single IRS to facilitate integrated sensing and communication(ISAC), in which the transmit signals at the BS are used for achieving both S&C functionalities, aided by the IRS through reflective beamforming. We present joint transmit signal and receiver processing designs for realizing efficient ISAC, and jointly optimize the transmit beamforming at the BS and reflective beamforming at the IRS to balance the fundamental performance tradeoff between S&C. Furthermore, we discuss multi-IRS networked ISAC, by particularly focusing on multi-IRS-enabled multi-link ISAC, multi-region ISAC, and ISAC signal routing, respectively. Finally, we highlight various promising research topics in this area to motivate future work.

关键词： integrated sensing and communication (ISAC) intelligent reflecting surface (IRS) non-line-of-sight (NLoS) sensing sensing and communication tradeoff

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Fairness of Large Music Models: From a Culturally Diverse Perspective 9

Fairness of Large Music Models: From a Culturally Diverse Pe...

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9th IEEE International Conference on Data Science in Cyberspace, DSC 2024

作者： Wang, Qinyuan Gu, Bruce Zhang, He Li, Yunfeng University of Sydney School of Music NSW2008 Australia Qilu University of Technology Shandong Academy of Sciences Key Laboratory of Computing Power Network and Information Security Ministry of Education Shandong Computer Science Center Jinan250031 China Shandong Fundamental Research Center for Computer Science Shandong Provincial Key Laboratory of Computer Networks Jinan250031 China CNPIEC KEXIN Ltd. Beijing100020 China

ISBN: (纸本)9798350391367

This paper explores the fairness of large music models in generating culturally diverse musical compositions, emphasizing the need for inclusivity and equity in AI-generated music. By analyzing popular datasets such as the Million Song Dataset and the Lakh MIDI Dataset, we identify a significant underrepresentation of non-Western musical elements. To address this, we adapt existing models to incorporate non-Western scales, rhythms, and instruments. The adapted models demonstrate a substantial improvement in generating culturally diverse music. Using AI-based synthetic listeners, we evaluate the impact of these adaptations on listener perception and engagement. Results indicate that music from the adapted models scores higher in terms of enjoyment, novelty, cultural resonance, and overall engagement compared to the original models. Our findings highlight the importance of cultural diversity in enhancing the user experience and ensuring ethical AI practices. The study also discusses challenges, such as dataset limitations and model adaptation complexity, and suggests directions for future research to further promote fairness and inclusivity in music AI. This work contributes to the development of more culturally aware and representative music generation systems, aiming to enrich the global musical landscape and foster greater appreciation of diverse musical traditions. © 2024 IEEE.

关键词： Adversarial machine learning

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Mini-Savi: Realistic Satellite network Simulation Platform Based on Open-Source Tools 4

Mini-Savi: Realistic Satellite Network Simulation Platform B...

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4th International Conference on Frontiers of computers and Communication Engineering, FCCE 2023

作者： Tang, Zhu Zhao, Jingjing Li, Hu Guo, Tianhang Wang, Quan Chen, Hongyan Zhao, Baokang National University of Defense Technology College of Computer Changsha China National Key Laboratory of Science and Technology on Information System Security Beijing China

ISBN: (纸本)9798350333169

Recently, mega satellite constellations are very popular for global instant messaging, and inter-satellite links are planned for delay reduction, leading to satellite network architecture finally. Although there are many satellite orbit simulation tools (e.g. STK, Savi) and network simulation tool (e.g. NS2/3, OPNET, Mininet, SNS3), none of them gives good consideration to both. In this paper, a satellite network and orbit integrated simulation platform (Mini-Savi) is proposed for large scale satellite constellation, which is based on the combination of network simulation tool Mininet and orbit simulation tool Savi. Finally, a typical satellite constellation Iridium system is employed for simulation verification. © 2023 IEEE.

关键词： Simulation platform

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Research on Collaborative Filtering Algorithm of E-commerce Based on Flink 13

Research on Collaborative Filtering Algorithm of E-commerce ...

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13th International Conference on Intelligent Control and information Processing, ICICIP 2025

作者： Zheng, Haohao Luo, Xiaonan Zhu, Xiaoshu School of Computer Science and Information Security Guilin University of Electronic Technology Guilin China Guangxi Key Laboratory of Image and Graphic Intelligent Processing Guilin University of Electronic Technology Guilin China

ISBN: (纸本)9798331516147

In online shopping, a person's interest in a product is closely related to whether they will purchase it Analyzing people's interest in various products on time, along with product recommendations, can increase purchase rates. However, in many cases, We only know user implicit feedback, such as browsing or favoriting items, and cannot directly access user explicit feedback, such as product ratings. Moreover, users have limited exposure to products, resulting in a sparse user- item matrix. The traditional Alternating Least Squares (ALS) algorithm can partially address the issue of matrix sparsity, but its accuracy in recommending products based on implicit feedback alone is insufficient This paper proposes an improved ALS algorithm that converts implicit feedback into explicit ratings. We calculate the browsing frequency of a product based on user browsing counts. For a given product, we represent user preference by summing the browsing frequencies of all products with browsing frequencies less than or equal to that of the product. Then, by multiplying the preference level by the product's maximum rating, we derive a product rating, achieving a transformation from implicit feedback to explicit scores. Additionally, traditional collaborative filtering algorithms often use offline data, which may not align with user current preferences. To improve the algorithm's accuracy, we also utilize Flink for real-time data processing, allowing us to capture the most recent feedback data. This study tests the approach using the UserBehavior dataset from the publicly available Tianchi dataset. Experimental results indicate improvements in both Root Mean Square Error (RMSE) and Mean Absolute Error (MAE). © 2025 IEEE.

关键词： Collaborative filtering

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STAN: A Spatio-Temporal Attention network for Traffic Forecasting 9

STAN: A Spatio-Temporal Attention Network for Traffic Foreca...

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9th IEEE Smart World Congress, SWC 2023

作者： Cai, Qingqiong Yang, Lu Xiao, Kunpeng Huang, Shenwei Nankai University College of Computer Science Tianjin300350 China Nankai University Tianjin Key Laboratory of Network and Data Security Technology Tianjin300350 China

ISBN: (纸本)9798350319804

Traffic prediction plays a vital role in intelligent transportation systems, such as flow control and route planning. As a classical spatial-temporal prediction task, timely accurate traffic prediction is challenging due to complicated spatial-temporal correlation in traffic data. Traffic data contains short-term and long-term temporal sequential correlations, as well as local and global spatial correlations. In addition, traffic patterns at different time steps and the distribution of traffic data at various nodes may differ, so models need to have the ability to capture these temporal and spatial heterogeneities. To solve these issues, we propose a novel traffic prediction model, called Spatio-Temporal Attention network (STAN). It models short-term and long-term temporal correlations using Gate Recurrent Unit (GRU) and Heterogeneity-Aware Temporal Self-Attention (HA-TSA) mechanism, respectively. It further models local and global spatial correlations using Graph Attention network (GAT) and Heterogeneity-Aware Spatial Self-Attention (HA-SSA) mechanism, respectively. Based on traditional self-attention mechanism, HA-TSA and HA-SSA further learn different parameters for different nodes and different time steps to capture temporal and spatial heterogeneities. Besides, in order to extract the periodicity of traffic data, time stamps are concatenated to the original traffic features as auxiliary information. STAN also adds a centrality encoding based on node's degree to measure the importance of a node in the traffic network, thus helping to discover similar traffic patterns between nodes. Experimental results on two benchmark traffic datasets show that compared with the state-of-the-art baseline method, Mean Absolute Errors (MAEs) of STAN decrease by 7.48% and 7.77%, and Root Mean Squared Errors (RMSEs) of STAN decrease by 1.89% and 5.19%, respectively. © 2023 IEEE.

关键词： attention mechanism spatial-temporal modeling traffic prediction

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Text Guided Road Marking Point Cloud Completion 6

Text Guided Road Marking Point Cloud Completion

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6th International Conference on Next Generation Data-Driven networks, NGDN 2024

作者： He, Xu Xu, Qihong Luo, Huan Fuzhou University College of Computer and Data Science Fuzhou China Fuzhou University Fujian Provincial Key Laboratory of Network Computing and Intelligent Information Processing Fuzhou China

ISBN: (纸本)9798350388374

With the continuous development of intelligent transportation technologies such as autonomous driving and navigation, accurate perception of road markings becomes crucial. However, due to limitations in sensor perspectives and obstacles blocking the view, the scanned point cloud of road markings is often incomplete, potentially leading to erroneous decisions in intelligent transportation systems. Therefore, it becomes imperative to recover complete road markings from these incomplete point clouds. This paper proposes a text-guided road marking completion method, which integrates text information with point cloud data using attention mechanisms. By leveraging text information to guide the network in completing road marking point clouds, the proposed method aims to enhance the perception accuracy and completeness of road markings. Experimental validation on road marking datasets demonstrates the effectiveness and feasibility of the proposed approach. © 2024 IEEE.

关键词： Road and street markings

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hcaPCG: A Heterogeneous and communication-avoid PCG with Jacobi preconditioner on SW26010-Pro architecture 29

hcaPCG: A Heterogeneous and communication-avoid PCG with Jac...

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29th IEEE International Conference on Parallel and Distributed Systems, ICPADS 2023

作者： Tian, Min Liu, Yue Wang, Li Chen, Yanyan Liu, Qi Pan, Jingshan Qilu University of Technology Shandong Academy of Sciences Key Laboratory of Computing Power Network and Information Security Ministry of Education Shandong Computer Science Center National Supercomputer Center in Jinan China Shandong Fundamental Research Center for Computer Science Shandong Provincial Key Laboratory of Computer Networks China Jinan Institute of Supercomputing Technology Jinan Key Laboratory of High Performance Computing China

ISBN: (纸本)9798350330717

Due to its efficiency and versatility, the preconditioned conjugate gradient algorithm has long been a staple in the realm of iterative linear system solvers. In this paper, we proposed an optimized preconditioned conjugate gradient algorithm tailored for the SW26010-Pro manycore processor, The main work includes: Optimizing data block sizes based on the processor's storage structure;combining thread-level and data-level parallelism, utilizing manual SIMD to improve efficiency;optimizing the memory access pattern by employing direct memory access and overlapping of computation and communication;employing a shared-memory approach to store long vectors across all cores. Furthermore, we design an accelerated algorithm for reduction operations, to avoid data communication. Experimental results show that the hcaPCG yields up to 28.1× speedups on average compared to the original implementation. © 2023 IEEE.

关键词： Communication-avoid Heterogeneous System Jacobi preconditioner PCG algorithm SW26010-Pro

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An SDN Traffic Engineering Approach Based on Traffic Unsupervised Contrastive Representation and Reinforcement Learning 6

An SDN Traffic Engineering Approach Based on Traffic Unsuper...

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6th International Conference on Next Generation Data-Driven networks, NGDN 2024

作者： Yang, Ruiyu Tang, Qi Guo, Yingya College of Computer and Data Science Fuzhou University Fuzhou China Fujian Provincial Key Laboratory of Network Computing and Intelligent Information Processing Fuzhou University Fuzhou China

ISBN: (纸本)9798350388374

With the rapid development of Internet technology and the continuous explosive growth of network traffic, Traffic Engineering (TE), as a viable method for optimizing network traffic distribution and improving network performance, attracts widespread attention from both industry and academia. Software Defined networks (SDN), which decouples the data plane and the control plane, realizes a flexible routing and improves the TE performance. Existing TE approaches in SDN mainly utilize Reinforcement Learning (RL) methods to learn the mapping relationship between network traffic and routing policies. However, due to the continuous expansion of network size and dynamic changes in traffic, the enlargement of traffic state space hinders RL from converging to the optimal routing policy, leading to a decline in network performance. To address these issues, this paper presents a TE method based on unsupervised contrastive representation and RL. This method first shrinks the original traffic state space by efficiently extracting traffic features through Contrastive Learning (CL), aiding quick convergence of RL. It then uses RL to directly learn the mapping from traffic features to traffic splitting policies. Finally, through numerous experiments on real network traffic and topology, it demonstrates that the proposed TE method can effectively achieve load balancing of network traffic under complex and volatile dynamic traffic demands, thereby enhancing network performance. © 2024 IEEE.

关键词： Reinforcement learning

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