Wearable health monitoring is a crucial technical tool that offers early warning for chronic diseases due to its superior portability and low power ***,most wearable health data is distributed across dfferent organiza...
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Wearable health monitoring is a crucial technical tool that offers early warning for chronic diseases due to its superior portability and low power ***,most wearable health data is distributed across dfferent organizations,such as hospitals,researchinstitutes,and companies,and can only be accessed by the owners of the data in compliance with data privacy *** first challenge addressed in this paper is communicating in a privacy-preserving manner among different *** second technical challenge is handling the dynamic expansion of the federation without model *** address the first challenge,we propose a horizontal federated learning method called Federated Extremely Random Forest(FedERF).Its contribution-based splitting score computing mechanism significantly mitigates the impact of privacy protection constraints on model *** on FedERF,we present a federated incremental learning method called Federated Incremental Extremely Random Forest(FedIERF)to address the second technical *** introduces a hardness-driven weighting mechanism and an importance-based updating scheme to update the existing federated model *** experiments show that FedERF achieves comparable performance with non-federated methods,and FedIERF effectively addresses the dynamic expansion of the *** opens up opportunities for cooperation between different organizations in wearable health monitoring.
To address the intelligent upgrading needs of neutron source accelerators, a digital twin visualization system was designed and developed based on digital twin technology. The virtual mapping model was constructed in ...
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Blockchain technology,with its attributes of decentralization,immutability,and traceability,has emerged as a powerful catalyst for enhancing traditional industries in terms of optimizing business ***,transaction perfo...
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Blockchain technology,with its attributes of decentralization,immutability,and traceability,has emerged as a powerful catalyst for enhancing traditional industries in terms of optimizing business ***,transaction performance and scalability has become the main challenges hindering the widespread adoption of *** to its inability to meet the demands of high-frequency trading,blockchain cannot be adopted in many *** improve the transaction capacity,researchers have proposed some on-chain scaling technologies,including lightning networks,directed acyclic graph technology,state channels,and shardingmechanisms,inwhich sharding emerges as a potential scaling ***,excessive cross-shard transactions and uneven shard workloads prevent the sharding mechanism from achieving the expected *** paper proposes a graphbased sharding scheme for public blockchain to efficiently balance the transaction *** crossshard transactions and evening-out workloads among shards,the scheme reduces transaction confirmation latency and enhances the transaction capacity of the ***,the scheme can achieve a high-frequency transaction as well as a better blockchain *** results show that the scheme effectively reduces the cross-shard transaction ratio to a range of 35%-56%and significantly decreases the transaction confirmation latency to 6 s in a blockchain with no more than 25 shards.
Network switches are critical elements in any network infrastructure for traffic forwarding and packet priority scheduling, which naturally become a target of network adversaries. Most attacks on switches focus on pur...
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Network switches are critical elements in any network infrastructure for traffic forwarding and packet priority scheduling, which naturally become a target of network adversaries. Most attacks on switches focus on purposely forwarding packets to the wrong network nodes or generating flooding. However, potential privacy leakage in the multi-level priority queue of switches has not been considered. In this paper, we are the first to discuss the multi-level priority queue security and privacy protection problem in switches. Observing that packet leaving order from a queue is strongly correlated to its priority, we introduce a policy inference attack that exploits specific priority-mapping rules between different packet priorities and priority sub-queues in the multi-level queues. Next, based on the policy inference result and the built-in traffic shaping strategy, a capacity inference attack with the error probability decaying exponentially in the number of attacks is presented. In addition, we propose a differentially private priority scheduling mechanism to defend against the above attacks in OpenFlow switches. Theoretical analysis proves that our proposed mechanism can satisfy \begin{document}$ \epsilon $\end{document}-differential privacy. Extensive evaluation results show that our mechanism can defend against inference attacks well and achieves up to 2.7 times priority process efficiency than a random priority scheduling strategy.
Diffusion models have become a powerful generative modeling paradigm, achieving great success in continuous data patterns. However, the discrete nature of text data results in compatibility issues between continuous d...
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Implicit Discourse Relation Recognition (IDRR), which infers discourse logical relations without explicit connectives, is one of the most challenging tasks in natural language processing (NLP). Recently, pre-trained l...
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The unprecedented Coronavirus Disease 2019 had spread and diffused in many countries and regions worldwide, posing a serious threat to people's health and lives. This review presents a systematic analysis and summ...
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Graph convolutional networks(GCNs)have received significant attention from various research fields due to the excellent performance in learning graph *** GCN performs well compared with other methods,it still faces **...
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Graph convolutional networks(GCNs)have received significant attention from various research fields due to the excellent performance in learning graph *** GCN performs well compared with other methods,it still faces *** a GCN model for large-scale graphs in a conventional way requires high computation and storage ***,motivated by an urgent need in terms of efficiency and scalability in training GCN,sampling methods have been proposed and achieved a significant *** this paper,we categorize sampling methods based on the sampling mechanisms and provide a comprehensive survey of sampling methods for efficient training of *** highlight the characteristics and differences of sampling methods,we present a detailed comparison within each category and further give an overall comparative analysis for the sampling methods in all ***,we discuss some challenges and future research directions of the sampling methods.
To solve the problem that it is difficult to accurately estimate the coherent parameters of distributed aperture radar under the condition of low signal-to-noise ratio, a distributed coherent synthesis method based on...
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1 Introduction Federated learning has emerged as a promising par-adigm for collaborative model training that facilitates cooperation among multiple parties while ensuring data privacy[1].Successful alignment of data a...
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1 Introduction Federated learning has emerged as a promising par-adigm for collaborative model training that facilitates cooperation among multiple parties while ensuring data privacy[1].Successful alignment of data across parties is crucial for effective federated learning[2].This alignment involves harmonizing heterogeneous data from different parties to identify shared data for joint model *** set intersection(PSI)is a technique that allows the alignment of common entities between parties without revealing additional ***,efficiently performing data alignment with PSI in federated learning[3],especially when dealing with highly unbalanced data,remains challenging due to the low efficiency.
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