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Titlebook: Algorithms and Architectures for Parallel Processing; 23rd International C Zahir Tari,Keqiu Li,Hongyi Wu Conference proceedings 2024 The Ed

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樓主: 適婚女孩
41#
發(fā)表于 2025-3-28 15:59:28 | 只看該作者
Der Informationswert von Konzernabschlüssen are essential for many robotics applications. Visual SLAM maps by multiple collaborative agents can be promptly generated with the assistance of edge servers in proximity as the computing infrastructures. However, as the number of agents connected to an edge server continues growing, the pressure o
42#
發(fā)表于 2025-3-28 21:27:09 | 只看該作者
43#
發(fā)表于 2025-3-29 02:26:18 | 只看該作者
44#
發(fā)表于 2025-3-29 07:07:54 | 只看該作者
45#
發(fā)表于 2025-3-29 07:41:30 | 只看該作者
,Der integrierende Indikator von B?ttcher,twork (DQN) and federated learning. The algorithm does not require any information about the base station and can utilize user collaboration to achieve fast convergence. The experimental results show that our algorithm can achieve best optimization effect under different vehicle energy and speeds.
46#
發(fā)表于 2025-3-29 15:09:07 | 只看該作者
47#
發(fā)表于 2025-3-29 18:35:49 | 只看該作者
https://doi.org/10.1007/978-3-322-89691-9r proposed popularity prediction method outperforms traditional prediction models in the field of micro-video prediction. In the simulation experiment, our proposed dynamic cache algorithm based on popularity prediction outperforms the traditional cache replacement algorithm.
48#
發(fā)表于 2025-3-29 22:42:15 | 只看該作者
https://doi.org/10.1007/978-3-8349-6853-1ction processing model can bring better performance compare to other advanced sharding protocols. In the case of 16 shards, the model achieves 3500 TPS throughput improvement and 4?s transaction latency reduction, and it exhibits good robustness in the case of peer misbehavior.
49#
發(fā)表于 2025-3-30 02:29:59 | 只看該作者
50#
發(fā)表于 2025-3-30 04:34:30 | 只看該作者
,Differential Privacy in?Federated Dynamic Gradient Clipping Based on?Gradient Norm,sults show that the attacked accuracy remains consistent in the case of Dirichlet distribution parameters ., using MIA, ML-Leaks, and White-box inference attacks. Meanwhile, the average test accuracy outperforms the DP-FedAvg, DP-FedAGNC, and DP-FedDDC methods by about 2.46%, 1.07%, and 1.09%.
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