HFedMS: Heterogeneous Federated Learning With Memorable Data Semantics in Industrial Metaverse

Zeng, Shenglai; Li, Zonghang; Zhang, Zhihao; Yu, Hongfang; Niyato, Dusit

doi:10.1109/tcc.2023.3254587

Cited by 32 publications

(19 citation statements)

References 38 publications

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“…Resilience, adaptation at run-time, and scalability are resulting advantages. FL is run on top of variants of Ensemble Learning (see figure 4) to address accuracy issues due to significantly heterogeneous class biases among local data [38] while creating FFMs [31]. Metarobotics benefits from FFMs when AI/ML models are intended to achieve similar tasks (e.g., picking and placing a rotor in an assembly line and grasping bottles in home settings) using new and legacy robots with limited data access.…”

Section: E Work-life-flexibility Between Industry and Societymentioning

confidence: 99%

Metarobotics for Industry and Society: Vision, Technologies, and Opportunities

Kaigom

2024

IEEE Trans. Ind. Inf.

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Metarobotics aims to combine next generation wireless communication, multi-sense immersion, and collective intelligence to provide a pervasive, itinerant, and non-invasive access and interaction with distant robotized applications. Industry and society are expected to benefit from these functionalities. For instance, robot programmers will no longer travel worldwide to plan and test robot motions, even collaboratively. Instead, they will have a personalized access to robots and their environments from anywhere, thus spending more time with family and friends. Students enrolled in robotics courses will be taught under authentic industrial conditions in real-time. This paper describes objectives of Metarobotics in society, industry, and in-between. It identifies and surveys technologies likely to enable their completion and provides an architecture to put forward the interplay of key components of Metarobotics. Potentials for self-determination, self-efficacy, and work-life-flexibility in robotics-related applications in Society 5.0, Industry 4.0, and Industry 5.0 are outlined.

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Section: E Work-life-flexibility Between Industry and Societymentioning

confidence: 99%

Metarobotics for Industry and Society: Vision, Technologies, and Opportunities

Kaigom

2024

IEEE Trans. Ind. Inf.

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“…The metaverse is often used to describe the future of the internet, consisting of a persistent, shared, 3D virtual space linked to a sentient virtual universe [ 11 , 12 , 13 , 14 ]. One of the key technologies of the Metaverse is digital twins, that means, digital replicas of a large number of physical environments in the Metaverse.…”

Section: Bridging Human Perception and Multimodal Content Generationmentioning

confidence: 99%

Emotion-Aware Scene Adaptation: A Bandwidth-Efficient Approach for Generating Animated Shorts

Yang,

Feng,

Cheng

et al. 2024

Sensors

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Semantic communication technology in the 6G wireless system focuses on semantic extraction in communication, that is, only the inherent meaning of the intention in the information. Existing technologies still have challenges in extracting emotional perception in the information, high compression rates, and privacy leakage due to knowledge sharing in communication. Large-scale generative-model technology could rapidly generate multimodal information according to user requirements. This paper proposes an approach that leverages large-scale generative models to create animated short films that are semantically and emotionally similar to real scenes and characters. The visual content of the data source is converted into text expression through semantic understanding technology; emotional clues from the data source media are added to the text form through reinforcement learning technology; and finally, a large-scale generative model is used to generate visual media, which is consistent with the semantics of the data source. This paper develops a semantic communication process with distinct modules and assesses the enhancements garnered from incorporating an emotion enhancement module. This approach facilitates the expedited generation of broad media forms and volumes according to the user’s intention, thereby enabling the creation of generated multimodal media within applications in the metaverse and in intelligent driving systems.

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“…Firstly, the leader uses the voting information from the last k blocks to quantify the degree of heterogeneity β based on (8). With the obtained β and a predefined α, the leader computes the committee size N , which must satisfy the equation in (2). Then, the leader generates a seed, which is the hash of the previous block, to feed into a Verifiable Random Function (VRF) [12].…”

Section: F Dynamic Committee Electionmentioning

confidence: 99%

“…Currently, several works have realized the integration of FL in the metaverse. For instance, the authors in [2] aim to solve the problem of data heterogeneity in FL within the industrial metaverse context. However, this is still a centralized scheme which is prone to SPoF and other mentioned issues.…”

Section: Introductionmentioning

confidence: 99%

BFLMeta: Blockchain-Empowered Metaverse with Byzantine-Robust Federated Learning

Truong,

Hoang,

2023

GLOBECOM 2023 - 2023 IEEE Global Communications Conference

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The emerging metaverse is envisioned as a virtual mapping of the real world, thus it would inevitably employ numerous Machine Learning (ML) frameworks to analyze and process massive data for the virtual-physical synchronization process. As a distributed ML paradigm, Federated Learning (FL) can naturally take advantage of numerous IoT, wearable devices, and edge, cloud servers under the metaverse infrastructure to train ML models with privacy guarantee. However, the large-scale and decentralized nature of the metaverse can pose significant challenges to traditional FL schemes, where there is a centralized server aggregating the local models received from local devices. It is not only vulnerable to Single Point of Failure (SPoF), but also lacks incentive mechanisms encouraging metaverse users to contribute their resource and data. In this paper, we propose BFLMeta, a blockchain-based FL scheme for the metaverse in which the aggregation process is performed in a decentralized manner, while the framework can estimate the non-IID degree of data to flexibly adjust blockchain committee size, thereby mitigating the impact of malicious aggregators. Security analysis shows that BFLMeta can resist SPoF, poisoning attack, privacy leakage, and sybil attack. Besides, our evaluation on computation, communication, and performance illustrates the efficiency of BFLMeta. Notably, BFLMeta can converge even with more than 50% poisoning nodes.

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HFedMS: Heterogeneous Federated Learning With Memorable Data Semantics in Industrial Metaverse

Cited by 32 publications

References 38 publications

Metarobotics for Industry and Society: Vision, Technologies, and Opportunities

Metarobotics for Industry and Society: Vision, Technologies, and Opportunities

Emotion-Aware Scene Adaptation: A Bandwidth-Efficient Approach for Generating Animated Shorts

BFLMeta: Blockchain-Empowered Metaverse with Byzantine-Robust Federated Learning

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