WiMi Hologram Cloud Develops Blockchain-Based Federated Learning Framework for Enhanced Privacy and Efficiency

WiMi Researches Reinforcement Learning-Based Blockchain Federated Learning Framework to Optimize Model Aggregation Strategy and Security - WiMi Hologram Cloud (NASDAQ:WIMI)

BEIJING, Nov. 8, 2024 /PRNewswire/ -- WiMi Hologram Cloud Inc. WIMI ("WiMi" or the "Company"), a leading global Hologram Augmented Reality ("AR") Technology provider, today announced the initiation of exploring the integration of Reinforcement Learning (RL) into the federated learning framework. RL, as a significant branch of machine learning, has become a crucial tool for optimizing the federated learning process due to its decision-making capabilities in complex environments. Reinforcement Learning is a machine learning approach that enables an intelligent agent to learn optimal strategies through interactions with the environment. In a blockchain-based federated learning framework utilizing reinforcement learning, the reinforcement learning algorithm can dynamically adjust the timing of model aggregation, selection of data participants, and transaction costs. This achieves a balance between information timeliness and data bias, as well as intelligent control over transaction costs, ultimately optimizing the overall learning performance. In federated learning, there can be significant differences in the datasets of different participants, known as the data bias problem. Additionally, model updates need to be aggregated at the appropriate timing to avoid outdated information affecting overall learning performance. The reinforcement learning algorithm can simulate interactions with the environment to learn when to upload model updates and how to select the most effective models for aggregation under different data distributions. This helps find the optimal balance between information timeliness and data bias. The cost of blockchain transactions, including the consumption of computational resources and network bandwidth, is another important consideration in federated learning. Reinforcement learning can intelligently predict network conditions, resource availability, and transaction priorities to dynamically adjust the frequency and scale of model aggregation. This ensures learning effectiveness while minimizing overall transaction costs. By applying reinforcement learning algorithms to optimize model aggregation strategies, not only does it significantly improve federated learning efficiency and model accuracy, but it also effectively reduces transaction costs. With the continuous advancement of technology, blockchain-based federated learning frameworks based on reinforcement learning will play a crucial role in various fields such as healthcare, financial services, and the Internet of Things (IoT), promoting the security, efficiency, and widespread adoption of artificial intelligence technology. For example, in the healthcare industry, this framework can facilitate data sharing among hospitals, research institutions, and patients, accelerating the development of disease diagnosis and treatment plans while strictly protecting individual privacy. In the financial services industry, it can assist banks and financial institutions in building more secure and efficient credit assessment and risk management models. In the field of IoT, it enables intelligent collaboration among devices, enhancing the overall network's responsiveness and intelligence level. WiMi's research on the blockchain-based federated learning framework using reinforcement learning represents a significant innovation at the intersection of artificial intelligence, blockchain technology, and reinforcement learning. It provides innovative approaches to address the trust, security, and efficiency issues faced by traditional federated learning. In the future, with further theoretical research and practical applications, the technological potential of blockchain-based federated learning using reinforcement learning will be more fully explored and widely applied in various industry sectors. About WiMi Hologram Cloud WiMi Hologram Cloud, Inc. WIMI is a holographic cloud comprehensive technical solution provider that focuses on professional areas including holographic AR automotive HUD software, 3D holographic pulse LiDAR, head-mounted light field holographic equipment, holographic semiconductor, holographic cloud software, holographic car navigation and others. Its services and holographic AR technologies include holographic AR automotive application, 3D holographic pulse LiDAR technology, holographic vision semiconductor technology, holographic software development, holographic AR advertising technology, holographic AR entertainment technology, holographic ARSDK payment, interactive holographic communication and other holographic AR technologies. Safe Harbor Statements This press release contains "forward-looking statements" within the Private Securities Litigation Reform Act of 1995. These forward-looking statements can be identified by terminology such as "will," "expects," "anticipates," "future," "intends," "plans," "believes," "estimates," and similar statements. Statements that are not historical facts, including statements about the Company's beliefs and expectations, are forward-looking statements. Among other things, the business outlook and quotations from management in this press release and the Company's strategic and operational plans contain forward-looking statements. The Company may also make written or oral forward-looking statements in its periodic reports to the US Securities and Exchange Commission ("SEC") on Forms 20-F and 6-K, in its annual report to shareholders, in press releases, and other written materials, and in oral statements made by its officers, directors or employees to third parties. Forward-looking statements involve inherent risks and uncertainties. Several factors could cause actual results to differ materially from those contained in any forward-looking statement, including but not limited to the following: the Company's goals and strategies; the Company's future business development, financial condition, and results of operations; the expected growth of the AR holographic industry; and the Company's expectations regarding demand for and market acceptance of its products and services. Further information regarding these and other risks is included in the Company's annual report on Form 20-F and the current report on Form 6-K and other documents filed with the SEC. All information provided in this press release is as of the date of this press release. The Company does not undertake any obligation to update any forward-looking statement except as required under applicable laws. View original content:https://www.prnewswire.com/news-releases/wimi-researches-reinforcement-learning-based-blockchain-federated-learning-framework-to-optimize-model-aggregation-strategy-and-security-302300038.html SOURCE WiMi Hologram Cloud Inc. Market News and Data brought to you by Benzinga APIs

WiMi Researches Reinforcement Learning-Based Blockchain Federated Learning Framework to Optimize Model Aggregation Strategy and Security By Investing.com

, /PRNewswire/ -- WiMi Hologram Cloud Inc. (NASDAQ: WiMi) ("WiMi" or the "Company"), a leading global Hologram Augmented Reality ("AR") Technology provider, today announced the initiation of exploring the integration of Reinforcement Learning (RL) into the federated learning framework. RL, as a significant branch of machine learning, has become a crucial tool for optimizing the federated learning process due to its decision-making capabilities in complex environments. Reinforcement Learning is a machine learning approach that enables an intelligent agent to learn optimal strategies through interactions with the environment. In a blockchain-based federated learning framework utilizing reinforcement learning, the reinforcement learning algorithm can dynamically adjust the timing of model aggregation, selection of data participants, and transaction costs. This achieves a balance between information timeliness and data bias, as well as intelligent control over transaction costs, ultimately optimizing the overall learning performance. In federated learning, there can be significant differences in the datasets of different participants, known as the data bias problem. Additionally, model updates need to be aggregated at the appropriate timing to avoid outdated information affecting overall learning performance. The reinforcement learning algorithm can simulate interactions with the environment to learn when to upload model updates and how to select the most effective models for aggregation under different data distributions. This helps find the optimal balance between information timeliness and data bias. The cost of blockchain transactions, including the consumption of computational resources and network bandwidth, is another important consideration in federated learning. Reinforcement learning can intelligently predict network conditions, resource availability, and transaction priorities to dynamically adjust the frequency and scale of model aggregation. This ensures learning effectiveness while minimizing overall transaction costs. By applying reinforcement learning algorithms to optimize model aggregation strategies, not only does it significantly improve federated learning efficiency and model accuracy, but it also effectively reduces transaction costs. With the continuous advancement of technology, blockchain-based federated learning frameworks based on reinforcement learning will play a crucial role in various fields such as healthcare, financial services, and the Internet of Things (IoT), promoting the security, efficiency, and widespread adoption of artificial intelligence technology. For example, in the healthcare industry, this framework can facilitate data sharing among hospitals, research institutions, and patients, accelerating the development of disease diagnosis and treatment plans while strictly protecting individual privacy. In the financial services industry, it can assist banks and financial institutions in building more secure and efficient credit assessment and risk management models. In the field of IoT, it enables intelligent collaboration among devices, enhancing the overall network's responsiveness and intelligence level. WiMi's research on the blockchain-based federated learning framework using reinforcement learning represents a significant innovation at the intersection of artificial intelligence, blockchain technology, and reinforcement learning. It provides innovative approaches to address the trust, security, and efficiency issues faced by traditional federated learning. In the future, with further theoretical research and practical applications, the technological potential of blockchain-based federated learning using reinforcement learning will be more fully explored and widely applied in various industry sectors. About WiMi Hologram Cloud WiMi Hologram Cloud, Inc. (NASDAQ:WiMi) is a holographic cloud comprehensive technical solution provider that focuses on professional areas including holographic AR automotive HUD software, 3D holographic pulse LiDAR, head-mounted light field holographic equipment, holographic semiconductor, holographic cloud software, holographic car navigation and others. Its services and holographic AR technologies include holographic AR automotive application, 3D holographic pulse LiDAR technology, holographic vision semiconductor technology, holographic software development, holographic AR advertising technology, holographic AR entertainment technology, holographic ARSDK payment, interactive holographic communication and other holographic AR technologies. Safe Harbor Statements This press release contains "forward-looking statements" within the Private Securities Litigation Reform Act of 1995. These forward-looking statements can be identified by terminology such as "will," "expects," "anticipates," "future," "intends," "plans," "believes," "estimates," and similar statements. Statements that are not historical facts, including statements about the Company's beliefs and expectations, are forward-looking statements. Among other things, the business outlook and quotations from management in this press release and the Company's strategic and operational plans contain forward ˆ'looking statements. The Company may also make written or oral forward ˆ'looking statements in its periodic reports to the US Securities and Exchange Commission ("SEC") on Forms 20 ˆ'F and 6 ˆ'K, in its annual report to shareholders, in press releases, and other written materials, and in oral statements made by its officers, directors or employees to third parties. Forward-looking statements involve inherent risks and uncertainties. Several factors could cause actual results to differ materially from those contained in any forward ˆ'looking statement, including but not limited to the following: the Company's goals and strategies; the Company's future business development, financial condition, and results of operations; the expected growth of the AR holographic industry; and the Company's expectations regarding demand for and market acceptance of its products and services. Further information regarding these and other risks is included in the Company's annual report on Form 20-F and the current report on Form 6-K and other documents filed with the SEC. All information provided in this press release is as of the date of this press release. The Company does not undertake any obligation to update any forward-looking statement except as required under applicable laws.

WiMi is Working on a Blockchain-Enhanced Federal Learning Privacy-Preserving Mechanism

BEIJING, Nov. 1, 2024 /PRNewswire/ -- WiMi Hologram Cloud Inc. (NASDAQ: WIMI) ("WiMi" or the "Company"), a leading global Hologram Augmented Reality ("AR") Technology provider, today announced that it is working on the Federal Learning on Blockchain (FLoBC), aiming to address two core challenges in the current data science field by integrating cutting-edge advances with blockchain technology and federated learning. The two challenges are data privacy protection and efficient training of large-scale machine learning models. Federated learning is a distributed machine-learning approach that allows models to be trained collaboratively without directly exchanging or centralizing raw data. This mechanism effectively protects user privacy by performing local model training on each participating node (e.g., mobile devices, enterprise servers, etc.) and sharing only updates to model parameters rather than raw data. However, the traditional federated learning framework faces problems such as inefficient communication and slow model convergence when facing large-scale, decentralized datasets, which is the key breakthrough direction of the blockchain-based federated learning framework researched by WiMi. Blockchain technology, with its tamper-proof, transparent and distributed nature, provides a new foundation of trust for data sharing and transactions. In the blockchain-based federated learning framework, blockchain not only serves as a distributed ledger to record every transaction of model update to ensure the transparency and verifiability of the training process, but also automates the management of verification, integration and incentive mechanism of model update through smart contracts, which facilitates collaboration and trust building in a decentralized environment. The blockchain-based federated learning framework utilizes the distributed nature of blockchain networks to design an efficient set of inter-node communication protocols and task scheduling algorithms that enable multiple nodes to process different parts of model training in parallel, significantly accelerating the training process. This mechanism is particularly suitable for processing large datasets and optimises computational resources. In addition, by constructing an autonomous learning network without a central coordinator, the blockchain-based federated learning framework ensures system resistance to a single point of failure. Each participating node can independently validate model updates, maintaining the consistency and stability of the entire network. WiMi's blockchain-based federated learning framework has a wide range of applications, involving sensitive data and large-scale model training, from financial risk control, and healthcare data analysis to personalized recommendation systems. However, many technical challenges need to be overcome to realize this vision, including improving cross-chain interoperability to expand data sources, enhancing encryption algorithms to protect the privacy of model updates further, and optimizing incentives to attract more participants to join the federated learning network. The blockchain-based federated learning framework is not only an attempt to deeply integrate the existing federated learning and blockchain technologies, but also a forward-looking response to the demand for privacy protection and efficient computation in the future data economy. It integrates the advantages of federated learning to protect data privacy by training models locally with the decentralized and transparent characteristics of blockchain, and creates an innovative path to maximize data value under the premise of protecting data privacy by ensuring the credibility of model updates, establishing an effective incentive mechanism, and strengthening security, which is the cutting-edge direction for the integration of current data science and privacy protection technologies. With the continuous development of technology and the deepening of application exploration, the blockchain-based federated learning framework is expected to become an important driving force to promote the development of artificial intelligence and data science. About WIMI Hologram Cloud WIMI Hologram Cloud, Inc. (NASDAQ:WIMI) is a holographic cloud comprehensive technical solution provider that focuses on professional areas including holographic AR automotive HUD software, 3D holographic pulse LiDAR, head-mounted light field holographic equipment, holographic semiconductor, holographic cloud software, holographic car navigation and others. Its services and holographic AR technologies include holographic AR automotive application, 3D holographic pulse LiDAR technology, holographic vision semiconductor technology, holographic software development, holographic AR advertising technology, holographic AR entertainment technology, holographic ARSDK payment, interactive holographic communication and other holographic AR technologies. Safe Harbor Statements This press release contains "forward-looking statements" within the Private Securities Litigation Reform Act of 1995. These forward-looking statements can be identified by terminology such as "will," "expects," "anticipates," "future," "intends," "plans," "believes," "estimates," and similar statements. Statements that are not historical facts, including statements about the Company's beliefs and expectations, are forward-looking statements. Among other things, the business outlook and quotations from management in this press release and the Company's strategic and operational plans contain forward-looking statements. The Company may also make written or oral forward-looking statements in its periodic reports to the US Securities and Exchange Commission ("SEC") on Forms 20-F and 6-K, in its annual report to shareholders, in press releases, and other written materials, and in oral statements made by its officers, directors or employees to third parties. Forward-looking statements involve inherent risks and uncertainties. Several factors could cause actual results to differ materially from those contained in any forward-looking statement, including but not limited to the following: the Company's goals and strategies; the Company's future business development, financial condition, and results of operations; the expected growth of the AR holographic industry; and the Company's expectations regarding demand for and market acceptance of its products and services. Further information regarding these and other risks is included in the Company's annual report on Form 20-F and the current report on Form 6-K and other documents filed with the SEC. All information provided in this press release is as of the date of this press release. The Company does not undertake any obligation to update any forward-looking statement except as required under applicable laws.

WiMi is Working on a Blockchain-Enhanced Federal Learning Privacy-Preserving Mechanism - WiMi Hologram Cloud (NASDAQ:WIMI)

BEIJING, Nov. 1, 2024 /PRNewswire/ -- WiMi Hologram Cloud Inc. WIMI ("WiMi" or the "Company"), a leading global Hologram Augmented Reality ("AR") Technology provider, today announced that it is working on the Federal Learning on Blockchain (FLoBC), aiming to address two core challenges in the current data science field by integrating cutting-edge advances with blockchain technology and federated learning. The two challenges are data privacy protection and efficient training of large-scale machine learning models. Federated learning is a distributed machine-learning approach that allows models to be trained collaboratively without directly exchanging or centralizing raw data. This mechanism effectively protects user privacy by performing local model training on each participating node (e.g., mobile devices, enterprise servers, etc.) and sharing only updates to model parameters rather than raw data. However, the traditional federated learning framework faces problems such as inefficient communication and slow model convergence when facing large-scale, decentralized datasets, which is the key breakthrough direction of the blockchain-based federated learning framework researched by WiMi. Blockchain technology, with its tamper-proof, transparent and distributed nature, provides a new foundation of trust for data sharing and transactions. In the blockchain-based federated learning framework, blockchain not only serves as a distributed ledger to record every transaction of model update to ensure the transparency and verifiability of the training process, but also automates the management of verification, integration and incentive mechanism of model update through smart contracts, which facilitates collaboration and trust building in a decentralized environment. The blockchain-based federated learning framework utilizes the distributed nature of blockchain networks to design an efficient set of inter-node communication protocols and task scheduling algorithms that enable multiple nodes to process different parts of model training in parallel, significantly accelerating the training process. This mechanism is particularly suitable for processing large datasets and optimises computational resources. In addition, by constructing an autonomous learning network without a central coordinator, the blockchain-based federated learning framework ensures system resistance to a single point of failure. Each participating node can independently validate model updates, maintaining the consistency and stability of the entire network. WiMi's blockchain-based federated learning framework has a wide range of applications, involving sensitive data and large-scale model training, from financial risk control, and healthcare data analysis to personalized recommendation systems. However, many technical challenges need to be overcome to realize this vision, including improving cross-chain interoperability to expand data sources, enhancing encryption algorithms to protect the privacy of model updates further, and optimizing incentives to attract more participants to join the federated learning network. The blockchain-based federated learning framework is not only an attempt to deeply integrate the existing federated learning and blockchain technologies, but also a forward-looking response to the demand for privacy protection and efficient computation in the future data economy. It integrates the advantages of federated learning to protect data privacy by training models locally with the decentralized and transparent characteristics of blockchain, and creates an innovative path to maximize data value under the premise of protecting data privacy by ensuring the credibility of model updates, establishing an effective incentive mechanism, and strengthening security, which is the cutting-edge direction for the integration of current data science and privacy protection technologies. With the continuous development of technology and the deepening of application exploration, the blockchain-based federated learning framework is expected to become an important driving force to promote the development of artificial intelligence and data science. About WIMI Hologram Cloud WIMI Hologram Cloud, Inc. WIMI is a holographic cloud comprehensive technical solution provider that focuses on professional areas including holographic AR automotive HUD software, 3D holographic pulse LiDAR, head-mounted light field holographic equipment, holographic semiconductor, holographic cloud software, holographic car navigation and others. Its services and holographic AR technologies include holographic AR automotive application, 3D holographic pulse LiDAR technology, holographic vision semiconductor technology, holographic software development, holographic AR advertising technology, holographic AR entertainment technology, holographic ARSDK payment, interactive holographic communication and other holographic AR technologies. Safe Harbor Statements This press release contains "forward-looking statements" within the Private Securities Litigation Reform Act of 1995. These forward-looking statements can be identified by terminology such as "will," "expects," "anticipates," "future," "intends," "plans," "believes," "estimates," and similar statements. Statements that are not historical facts, including statements about the Company's beliefs and expectations, are forward-looking statements. Among other things, the business outlook and quotations from management in this press release and the Company's strategic and operational plans contain forward-looking statements. The Company may also make written or oral forward-looking statements in its periodic reports to the US Securities and Exchange Commission ("SEC") on Forms 20-F and 6-K, in its annual report to shareholders, in press releases, and other written materials, and in oral statements made by its officers, directors or employees to third parties. Forward-looking statements involve inherent risks and uncertainties. Several factors could cause actual results to differ materially from those contained in any forward-looking statement, including but not limited to the following: the Company's goals and strategies; the Company's future business development, financial condition, and results of operations; the expected growth of the AR holographic industry; and the Company's expectations regarding demand for and market acceptance of its products and services. Further information regarding these and other risks is included in the Company's annual report on Form 20-F and the current report on Form 6-K and other documents filed with the SEC. All information provided in this press release is as of the date of this press release. The Company does not undertake any obligation to update any forward-looking statement except as required under applicable laws. View original content:https://www.prnewswire.com/news-releases/wimi-is-working-on-a-blockchain-enhanced-federal-learning-privacy-preserving-mechanism-302294207.html SOURCE WiMi Hologram Cloud Inc. Market News and Data brought to you by Benzinga APIs

WiMi develops blockchain-based federated learning framework By Investing.com

BEIJING - WiMi Hologram Cloud Inc. (NASDAQ: WIMI), a global leader in holographic augmented reality (AR) technologies, is developing a new blockchain-based federated learning system, the company announced today. The Federal Learning on Blockchain (FLoBC) initiative aims to tackle data privacy protection and the efficient training of large-scale machine learning models. The company's approach to federated learning allows for collaborative model training across various nodes - such as mobile devices and enterprise servers - without centralizing or directly exchanging raw data. This process is designed to protect user privacy by sharing only model parameter updates rather than the raw data itself. WiMi's framework addresses the inefficiencies and slow convergence rates associated with traditional federated learning when dealing with large, decentralized datasets. The application of blockchain technology ensures transparency and verifiability in the training process, with smart contracts facilitating the verification, integration, and incentivization of model updates. The system is engineered to utilize blockchain's distributed nature for creating efficient communication protocols and task scheduling algorithms, which enable parallel processing of model training tasks, thereby accelerating the process. The framework's design also prevents a single point of failure by allowing each node to independently validate model updates, ensuring network consistency and stability. Potential applications for WiMi's framework span various fields that handle sensitive data and require large-scale model training, including financial risk control, healthcare data analysis, and personalized recommendation systems. Despite its promise, the company acknowledges that there are technical hurdles to overcome, such as improving cross-chain interoperability, enhancing encryption algorithms, and optimizing incentives to encourage broader participation. WiMi's initiative represents a convergence of federated learning and blockchain technologies, with the goal of advancing privacy protection and computational efficiency in the data economy. This development is anticipated to contribute significantly to the fields of artificial intelligence and data science. WiMi Hologram Cloud Inc. specializes in a range of holographic AR technologies and solutions, including automotive HUD software, 3D holographic pulse LiDAR, and holographic semiconductor technology, among others. This news is based on a press release statement. The company's forward-looking statements involve risks and uncertainties, and actual results may differ materially from those projected. In other recent news, WiMi Hologram Cloud Inc. has been making considerable strides in technological advancements. The company has unveiled a machine learning-based blockchain hybrid consensus algorithm, designed to enhance the security and efficiency of blockchain networks. This algorithm focuses on detecting security threats and preventing attacks through continuous monitoring and anomaly detection technologies. In addition, WiMi has introduced the WiMi HoloAR Lens, an AR headset with advanced features such as a wide 63-degree field of view, 600-inch screen display, and compatibility with Steam games. This lightweight headset aims to enhance user experience in various scenarios, including travel, virtual communication, and home entertainment. Furthermore, the company has developed a blockchain-based identity management model which uses graph theory and blockchain frameworks to enhance security in cloud computing environments. This model features a decentralized and tamper-proof ledger, unique identifiers for users, and smart contracts to automate the authentication process. WiMi has also revealed an enhanced blockchain consensus mechanism designed to improve network efficiency and security, and a novel innovation in blockchain data security that merges machine learning with fully homomorphic encryption. These recent developments highlight WiMi's continued expansion into blockchain technology, complementing its existing suite of services and products in the holographic AR space. As WiMi Hologram Cloud Inc. (NASDAQ: WIMI) advances its blockchain-based federated learning system, investors may find value in examining the company's financial health and market performance. According to InvestingPro data, WIMI's market capitalization stands at $84.46 million, reflecting its position in the emerging holographic AR technology sector. Despite the company's innovative efforts, InvestingPro Tips reveal that WIMI is not currently profitable over the last twelve months. This aligns with the company's focus on research and development in cutting-edge technologies, which often requires significant investment before generating returns. However, it's worth noting that analysts anticipate sales growth in the current year, which could be driven by the potential commercialization of technologies like the FLoBC initiative. Interestingly, WIMI holds more cash than debt on its balance sheet, suggesting a degree of financial stability as it pursues its technological developments. This financial cushion may provide the company with the flexibility needed to continue its research and development efforts in blockchain and federated learning systems. For investors considering WIMI's long-term potential, it's important to note that the stock's price has fallen significantly over the last five years. However, with a price-to-book ratio of 0.79 as of the last twelve months ending Q2 2024, the stock may be trading below its book value, potentially indicating an undervaluation. These insights offer a glimpse into WIMI's financial landscape. InvestingPro provides 8 additional tips for WIMI, offering a more comprehensive analysis for those looking to delve deeper into the company's investment potential.