WiMi Researches Reinforcement Learning-Based Blockchain Federated Learning Framework to Optimize Model Aggregation Strategy and Security
WiMi Researches Reinforcement Learning-Based Blockchain Federated Learning Framework to Optimize Model Aggregation Strategy and Security
BEIJING, Nov. 8, 2024 /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.
2024年11月8日,北京/新華社/ - 微美全息雲公司(NASDAQ:WiMi)(簡稱"WiMi"或"公司"),是全球領先的全息增強現實(AR)技術提供商,今天宣佈開始探索將強化學習(RL)整合到聯邦學習框架中。作爲機器學習的重要分支,RL已經成爲優化聯邦學習過程的重要工具,因爲它在複雜環境中具有決策能力。
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.
強化學習是一種機器學習方法,使智能代理通過與環境互動學習最佳策略。在利用強化學習的基於區塊鏈的聯邦學習框架中,強化學習算法可以動態調整模型聚合的時機,數據參與者的選擇和交易成本。這實現了信息及時性和數據偏差之間的平衡,以及對交易成本的智能控制,最終優化整體學習性能。
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 Additionally, model updates need to be aggregated at the appropriate timing to avoid outdated information affecting overall learning performance.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.
在聯邦學習中,不同參與者的數據集可能存在顯著差異,被稱爲數據偏差問題。此外,需要在適當的時機對模型更新進行聚合,以避免過時信息影響整體學習性能。強化學習算法可以模擬與環境的互動,學習何時上傳模型更新以及如何選擇在不同數據分佈下進行聚合的最有效模型。這有助於找到信息及時性和數據偏差之間的最佳平衡。區塊鏈交易成本包括計算資源和網絡帶寬的消耗等,在聯邦學習中也是另一個重要考慮因素。強化學習可以智能預測網絡條件、資源可用性和交易優先級,動態調整模型聚合的頻率和規模。這確保了學習的效果,同時最小化整體交易成本。通過應用強化學習算法來優化模型聚合策略,不僅顯著提高了聯邦學習的效率和模型準確性,還有效降低了交易成本。
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.
隨着科技不斷進步,基於強化學習的基於區塊鏈的聯邦學習框架將在醫療保健、金融服務和物聯網等各個領域發揮關鍵作用,促進人工智能技術的安全性、效率和廣泛應用。例如,在醫療行業,這一框架可以促進醫院、研究機構和患者之間的數據共享,加快疾病診斷和治療方案的開發,同時嚴格保護個人隱私。在金融服務行業,它可以幫助銀行和金融機構建立更安全、更高效的信用評估和風險管理模型。在物聯網領域,它實現設備之間的智能協作,增強整個網絡的響應性和智能水平。
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.
WiMi對基於區塊鏈的強化學習聯邦學習框架的研究代表了人工智能、區塊鏈技術和強化學習交叉領域的重大創新。它提供了創新方法來解決傳統聯邦學習面臨的信任、安全和效率問題。未來,隨着進一步的理論研究和實際應用,基於強化學習的區塊鏈聯邦學習技術的潛力將得到更充分的探索,並被廣泛應用於各個行業領域。
About WiMi Hologram Cloud
關於微美全息雲
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.
納斯達克:WiMi全息雲,Inc.是一家全息雲綜合技術解決方案供應商,專注於全息AR汽車HUD軟件、3D全息脈衝激光雷達、頭戴式光場全息設備、全息半導體、全息雲軟件、全息汽車導航等專業領域。其服務和全息AR技術包括全息AR汽車應用、3D全息脈衝激光雷達技術、全息視覺半導體技術、全息軟件開發、全息AR廣告技術、全息AR娛樂技術、全息ARSDk支付、互動全息通信等其他全息AR技術。
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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.
本新聞稿包含「前瞻性聲明」,根據1995年《私人證券訴訟改革法案》。這些前瞻性聲明可以通過「將」、「期望」、「預計」、「未來」、「意圖」、「計劃」、「相信」、「估計」和類似語句來識別。不是歷史事實的聲明,包括有關公司信念和期望的聲明,都屬於前瞻性聲明。除其他事項外,在本新聞稿中業務前景和管理層報價以及公司的戰略和運營計劃中包含前瞻性聲明。公司還可能在其向美國證券交易委員會(SEC)提交的20-F和6-k表格、年度股東報告、新聞稿和其他書面材料以及其高管、董事或員工向第三方作出的口頭陳述中發佈書面或口頭的前瞻性聲明。前瞻性聲明涉及固有的風險和不確定性。有幾個因素可能會導致任何前瞻性聲明所包含的實際結果與其中所含的不同,包括但不限於以下內容:公司的目標和戰略;公司未來的業務發展、財務狀況和運營結果;AR全息產業的預期增長;以及公司對其產品和服務的需求和市場接受程度的預期。
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.
有關這些和其他風險的進一步信息包含在公司的20-F年度報告和6-K和其他文件中。本新聞稿所提供的所有信息均截至本新聞稿的發佈日期。除適用法律規定外,公司無義務更新任何前瞻性聲明。
SOURCE WiMi Hologram Cloud Inc.
來源:WiMi全息雲
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