阻挡
发表于 2025-3-25 06:38:01
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成份
发表于 2025-3-25 10:43:47
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畸形
发表于 2025-3-25 12:27:35
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harmony
发表于 2025-3-25 16:39:13
Privacy in Social Edge,Health Detection Systems (AHDS). In particular, we present FedSens, a new federated learning framework dedicated to addressing the imbalanced data problem in AHDS applications with explicit considerations of participant privacy and device resource constraints.
Annotate
发表于 2025-3-25 22:49:32
Conclusion and Remaining Challenges,fferent research communities (e.g., distributed computing, IoT and cyber-physical systems, social computing, AI, human-computer interaction, privacy and security, etc.) will keep on increasing and more fundamental and interesting research work will be carried out in future.
无能的人
发表于 2025-3-26 02:12:41
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Negotiate
发表于 2025-3-26 07:31:08
Social Edge Trends and Applications, brings new opportunities for human-centric applications (e.g., social sensing, smart mobile computing, edge intelligence). By coupling those applications with edge computing, the individually owned edge devices form a federation of computational nodes where the data collected from them can be proce
切掉
发表于 2025-3-26 12:03:15
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引起
发表于 2025-3-26 13:13:30
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晚来的提名
发表于 2025-3-26 18:29:53
Real-Time AI in Social Edge,m. In this chapter, we shift our focus to real-time AI in the social edge that investigates the challenge of building time-sensitive AI models in SEC. In particular, we focus on a widely adopted AI model—deep neural networks (DNN), and review a novel optimal batching algorithm called EdgeBatch that