When Moving Target Defense Meets Attack Prediction in Digital Twins: A Convolutional and Hierarchical Reinforcement Learning Approach

Despite recent significant advancements in large language models (LLMs) for medical services, the deployment difficulties of LLMs in e-healthcare hinder complex medical applications in the Internet of Medical Things (IoMT). People are increasingly concerned about e-healthcare risks and privacy protection. Existing LLMs face difficulties in providing accurate medical questions and answers (Q&As) and meeting the deployment resource demands in the IoMT. To address these challenges, we propose MedMixtral 8x7B, a new medical LLM based on the mixture-of-experts (MoE) architecture with an offloading strategy, enabling deployment on the IoMT, improving the privacy protection for users. Additionally, we find that the significant factors affecting latency include the method of device interconnection, the location of offloading servers, and the speed of the disk.

Section: Ai Used In Communicationmentioning

confidence: 99%

Efficient Inference Offloading for Mixture-of-Experts Large Language Models in Internet of Medical Things

Yuan,

Kong,

Luo

et al. 2024

“…We utilize a gated recurrent unit (GRU) to extract dynamic features from BVP since it is time series data. Compared with long short-term memory (LSTM) [47], GRU can achieve comparable results using fewer parameters. To prevent overfitting, we introduce a dropout layer.…”

Section: Model Structurementioning

confidence: 99%

Personalized Federated Learning Based on Bidirectional Knowledge Distillation for WiFi Gesture Recognition

Geng,

Deng,

Zhang

et al. 2023

WiFi-based human gesture recognition has a wide range of applications in smart homes. Existing methods train gesture classification models by collecting large amounts of WiFi signal data in a centralized manner. However, centralized training faces challenges, including high communication overhead and the risk of data privacy leakage. Federated learning (FL) provides an opportunity to collaboratively train and share models without compromising data privacy. One of the main challenges FL faces is data that is non-Independent and Identically Distributed (non-IID) across clients. Specifically, in the gesture recognition scenario, since the transmission of WiFi signals is susceptible to cross-environment and cross-person interference, non-IID mainly manifests itself as a cross-domain problem. Cross-domain makes the knowledge learned between client models incompatible. Therefore, in the cross-domain scenario, effectively extracting and combining the knowledge learned by the client is a challenge. To solve this problem, we propose pFedBKD, a novel personalized federated learning scheme via bidirectional distillation. First, the knowledge that is beneficial to the client is extracted from the shared server model through knowledge distillation in the client, which helps train the personalized model of the client. Second, the server adaptively adjusts the aggregation weights according to the deviation between the shared model and the client’s local model so that the server’s shared model can extract more public knowledge. We conduct experiments on multiple open-source datasets. Experimental results show that our method is superior to existing methods and effectively alleviates the problem of reduced model recognition accuracy caused by cross-domain challenges.

“…In various fields such as social media, computer science, biology, management science, and engineering, complex systems are often represented in the form of complex networks. These complex networks can be depicted as graphs in graph theory, where nodes in the graph represent entities in the system, and edges represent interactions between entities [1]. For example, in online social networks, nodes may represent users on the platform, and edges could signify friendship relationships or shared interests [2,3], and in a blockchain network, nodes represent participants such as individuals, companies, or servers that execute transactions, while edges represent the connections or interactions between these nodes, often symbolizing specific transactions.…”

Section: Introductionmentioning

confidence: 99%

CSIM: A Fast Community Detection Algorithm Based on Structure Information Maximization

Liu,

Tang

et al. 2024

Community detection has been a subject of extensive research due to its broad applications across social media, computer science, biology, and complex systems. Modularity stands out as a predominant metric guiding community detection, with numerous algorithms aimed at maximizing modularity. However, modularity encounters a resolution limit problem when identifying small community structures. To tackle this challenge, this paper presents a novel approach by defining community structure information from the perspective of encoding edge information. This pioneering definition lays the foundation for the proposed fast community detection algorithm CSIM, boasting an average time complexity of only O(nlogn). Experimental results showcase that communities identified via the CSIM algorithm across various graph data types closely resemble ground truth community structures compared to those revealed via modularity-based algorithms. Furthermore, CSIM not only boasts lower time complexity than greedy algorithms optimizing community structure information but also achieves superior optimization results. Notably, in cyclic network graphs, CSIM surpasses modularity-based algorithms in effectively addressing the resolution limit problem.