WiMi Announces That It Is Working On The Federal Learning On Blockchain, Aiming To Address Two Core Challenges In The Current Data Science Field By Integrating Cutting-edge Advances With Blockchain Technology And Federated Learning
WiMi Announces That It Is Working On The Federal Learning On Blockchain, Aiming To Address Two Core Challenges In The Current Data Science Field By Integrating Cutting-edge Advances With Blockchain Technology And Federated Learning
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.
微美全息雲公司（納斯達克：WIMI）（"WiMi"或"公司"）是全球領先的全息增強現實（"AR"）技術提供商，今日宣佈正在開展區塊鏈技術上的聯邦學習（FLoBC），旨在通過將尖端進展與區塊鏈技術和聯邦學習相結合，解決當前數據科學領域的兩個核心挑戰。 這兩個挑戰是數據隱私保護和大規模機器學習模型的高效訓練。
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.
聯邦學習是一種分佈式機器學習方法，可以使模型在不直接交換或集中原始數據的情況下進行協作訓練。該機制通過在每個參與節點（例如移動設備、企業服務器等）上進行本地模型訓練，僅共享模型參數的更新而不是原始數據，從而有效保護用戶隱私。然而，傳統的聯邦學習框架在面對大規模分佈式數據集時，存在通信效率低、模型收斂速度慢等問題，這是微美研究的基於區塊鏈的聯邦學習框架的關鍵突破方向。
