WiMi Announced a Federated Learning Framework Based on Layered and Sharded Blockchain Technology
In the federated learning framework based on layered and sharded blockchain technology, the IoT network is finely divided into a multi-layer structure, and each layer is subdivided into multiple shards, aiming to optimize the information interaction and processing efficiency. The strategy of multiple layers and multiple shards enables the communication between nodes to be restricted to the same shard, which significantly reduces the complexity of information interaction and greatly reduces the global communication cost. And the sharding mechanism ensures that each shard can execute local training tasks independently and in parallel, accelerating the overall learning process. At the same time, cross-shard data exchange is performed only when the model parameters are updated, which not only ensures the training efficiency, but also further strengthens the data security and privacy protection.
In response to the abnormal or malicious behavior that may occur in federated learning, WiMi has developed a highly adaptive consensus algorithm. The algorithm is able to accurately identify and reject abnormal models, effectively resist interference caused by malicious or erroneous data, and ensure the accuracy and reliability of learning results. The application of blockchain technology records the transaction details of every model update, provides an untampered audit log, enhances system transparency, and establishes a foundation of trust among participants.
With the help of encryption and distributed ledger technology, WiMi's federated learning framework ensures the security of data during transmission and storage, effectively guarding against data leakage and tampering. Distributed ledger uses cryptographic techniques to protect the security and integrity of data, such as hash functions, public and private key encryption, and other techniques. These techniques prevent problems such as data tampering, forgery, and theft. In addition, data privacy can be further protected by restricting user access to data through smart contracts or other permission control mechanisms.
The sharding and parallel processing mechanism greatly improves computational efficiency and reduces latency, which is particularly suitable for real-time learning scenarios of large-scale IoT devices. The flexible layering and sharding design enables the system to seamlessly adapt to all kinds of network environments from small LANs to global scale. This design not only improves the scalability of the system, but also enables it to be flexibly deployed and operated in different network environments to meet diverse needs.
The federated learning framework builds an efficient, secure, and scalable IoT learning platform through layered and sharded technologies, adaptive consensus algorithms, encryption and distributed ledger technologies, and flexible computing architectures, laying a solid foundation for future large-scale machine learning applications. The federated learning framework based on layered and sharded blockchain not only overcomes the limitations of traditional federated learning, but also creates a brand-new path to safer and more efficient data collaboration, which is a profound insight and layout for future smart life. Whether it is smart home, smart city, or Industry 4.0, federated learning technology based on layered and sharded blockchain shows broad application prospects, and is expected to promote the digital transformation of all industries, and build a smarter, safer, and more efficient future society. In the era of the Internet of Everything, WiMi will also continue to explore and practice, leading the way to a new era of smarter, safer and more efficient data collaboration.
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