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1. Bodyless block propagation: TPS fully scalable blockchain with pre-validation NSTL国家科技图书文献中心

Chonghe Zhao |  Shengli Zhang... -  《Future generations computer systems》 - 2025,163(Feb.) - 107516.1～107516.16 - 共16页

摘要：Despite numerous prior attempts to boost |  transaction per second (TPS) of blockchain system, most of |  them were at a price of degraded decentralization and |  security. In this paper, we propose a bodyless block |  propagation (BBP) scheme for which the blockbody is not
关键词： Blockchain |  Bodyless block |  TPS |  Block propagation |  Block validation
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2. Expanding SafeSU capabilities by leveraging security frameworks for contention monitoring in complex SoCs NSTL国家科技图书文献中心

Pablo Andreu |  Sergi Alcaide... -  《Future generations computer systems》 - 2025,163(Feb.) - 107518.1～107518.9 - 共9页

摘要：The increased performance requirements of |  applications running on safety-critical systems have led to |  the use of complex platforms with several CPUs, GPUs | , and AI accelerators. However, higher platform and |  system complexity challenge performance verification
关键词： Safety |  QoS |  Network on chip |  Mixed-criticality |  Multicore
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3. Global reduction for geo-distributed MapReduce across cloud federation NSTL国家科技图书文献中心

Thouraya Gouasmi |  Ahmed Hadj Kacem -  《Future generations computer systems》 - 2025,162(Jan.) - 107492.1～107492.12 - 共12页

摘要：Geo-distributed Bigdata processing is |  increasing day by day, resulting in the origins of data |  that are geographically distributed in different |  countries and hold datacenters (DCs) across the globe, and |  also the applications that use different sites to
关键词： MapReduce |  Cloud federation |  BigData |  Geo-distributed scheduling |  Cost optimization
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4. FedDA: Resource-adaptive federated learning with dual-alignment aggregation optimization for heterogeneous edge devices NSTL国家科技图书文献中心

Shaohua Cao |  Huixin Wu... -  《Future generations computer systems》 - 2025,163(Feb.) - 107551.1～107551.14 - 共14页

摘要：Federated learning (FL) is an emerging |  distributed learning paradigm that allows multiple clients |  to collaborate on training a global model without |  sharing their local data. However, in practical |  heterogeneous edge device scenarios, FL faces the challenges
关键词： Federated learning |  Resource-adaptive |  Aggregation optimization |  Client heterogeneity |  Edge intelligence
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5. SFML: A personalized, efficient, and privacy-preserving collaborative traffic classification architecture based on split learning and mutual learning NSTL国家科技图书文献中心

Jiaqi Xia |  Meng Wu... -  《Future generations computer systems》 - 2025,162(Jan.) - 107487.1～107487.16 - 共16页

摘要：Traffic classification is essential for |  network management and optimization, enhancing user |  experience, network performance, and security. However | , evolving technologies and complex network environments |  pose challenges. Recently, researchers have turned to
关键词： Federated learning |  Network traffic classification |  Split learning |  Mutual learning
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6. Small models, big impact: A review on the power of lightweight Federated Learning NSTL国家科技图书文献中心

Pian Qi |  Diletta Chiaro... -  《Future generations computer systems》 - 2025,162(Jan.) - 107484.1～107484.15 - 共15页

摘要：Federated Learning (FL) enhances Artificial |  Intelligence (AI) applications by enabling individual devices |  to collaboratively learn shared models without |  uploading local data with third parties, thereby |  preserving privacy. However, implementing FL in real-world
关键词： Federated Learning |  Device heterogeneity |  Constrained devices |  Lightweight federated learning |  Tiny federated learning
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7. Self-aware collaborative edge inference with embedded devices for IIoT NSTL国家科技图书文献中心

Yifan Chen |  Zhuoquan Yu... -  《Future generations computer systems》 - 2025,163(Feb.) - 107535.1～107535.13 - 共13页

摘要：Edge inference and other compute-intensive |  industrial Internet of Things (IIoT) applications suffer |  from a bad quality of experience due to the limited |  and heterogeneous computing and communication |  resources of embedded devices. To tackle these issues, we
关键词： IIoT |  Model partitioning |  Self-aware |  Collaborative edge inference |  Inference efficiency
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8. ServlessSimPro: A comprehensive serverless simulation platform NSTL国家科技图书文献中心

Han Cao |  Jinquan Zhang... -  《Future generations computer systems》 - 2025,163(Feb.) - 107558.1～107558.11 - 共11页

摘要：Serverless computing represents an emerging |  paradigm within cloud computing, characterized by the |  fundamental concept of enabling developers to run |  applications without the need for concerns related to the |  management of underlying servers. Although there are
关键词： Serverless |  Simulation |  Cloud computing |  Scheduling
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9. Energy-efficiency optimization for heterogeneous computing-assisted NOMA-MEC edge AI tasks NSTL国家科技图书文献中心

Rui She |  Yuting Wu... -  《Future generations computer systems》 - 2025,162(Jan.) - 107458.1～107458.13 - 共13页

摘要：Edge artificial intelligence (AI) is an |  emerging paradigm that leverages edge computing to pave |  the last-mile delivery of AI. To satisfy the |  increasing demand for high-performance computing and low |  latency of edge service, heterogeneous computing
关键词： Energy-efficiency |  Edge AI |  Heterogeneous computing |  MEC-NOMA |  CPU-NPU |  Task offloading
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10. HashGrid: An optimized architecture for accelerating graph computing on FPGAs NSTL国家科技图书文献中心

Amin Sahebi |  Marco Procaccini... -  《Future generations computer systems》 - 2025,162(Jan.) - 107497.1～107497.15 - 共15页

摘要：Large-scale graph processing poses challenges |  due to its size and irregular memory access patterns | , causing performance degradation in common architectures | , such as CPUs and GPUs. Recent research includes |  accelerating graph processing using Field Programmable Gate
关键词： Big graph computing |  High-performance computing |  FPGA design |  Large-scale graph |  Graph partitioning
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