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

Rhea-AI Summary

WiMi Hologram Cloud (NASDAQ: WIMI) announced its work on Federal Learning on Blockchain (FLoBC), combining blockchain technology with federated learning to address data privacy protection and efficient training of large-scale machine learning models. The framework enables collaborative model training without direct data exchange, using blockchain's distributed nature for transparent record-keeping and smart contracts for automated management. The system allows parallel processing across multiple nodes, accelerating training processes while maintaining privacy and eliminating single points of failure. Applications span financial risk control, healthcare data analysis, and personalized recommendation systems.

Positive

• Development of innovative blockchain-based federated learning technology
• Potential applications in multiple high-value sectors (finance, healthcare)
• Enhanced data privacy protection capabilities
• Improved efficiency in large-scale machine learning model training

Negative

• Technical challenges in cross-chain interoperability remain unresolved
• System optimization and encryption improvements still needed
• Participant incentivization issues yet to be addressed

Insights

Tech Expert

This research initiative, while innovative in combining blockchain with federated learning, has minimal immediate impact on WiMi's financial performance or stock value. The project is still in early development stages, with many technical challenges yet to be overcome. Key limitations include:

• No clear timeline for commercialization
• Unspecified resource allocation
• Absence of strategic partnerships or revenue projections

While the technology could potentially address important issues in data privacy and machine learning, the announcement lacks concrete implementation details or business metrics. For a company with a market cap of $86.2M, investors should focus more on near-term revenue-generating products rather than early-stage R&D projects.

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.

 

 

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SOURCE WiMi Hologram Cloud Inc.

FAQ

What is WiMi's Federal Learning on Blockchain (FLoBC) project?

FLoBC is WiMi's blockchain-based federated learning framework that combines blockchain technology with federated learning to protect data privacy and enable efficient training of large-scale machine learning models without direct data exchange.

How does WIMI's blockchain-based federated learning framework protect privacy?

The framework performs local model training on participating nodes and shares only model parameter updates rather than raw data, while using blockchain to ensure transparency and verification of the training process.

What are the main applications of WIMI's blockchain-based federated learning system?

The system can be applied in financial risk control, healthcare data analysis, and personalized recommendation systems where sensitive data handling and large-scale model training are required.