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1.发明公开
公开(公告)号：EP3932043A1
公开(公告)日：2022-01-05
申请号：EP21747157.2
申请日：2021-01-14
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2.发明公开
公开(公告)号：EP3928208A1
公开(公告)日：2021-12-29
申请号：EP20715224.0
申请日：2020-02-20
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3.发明公开
公开(公告)号：EP3564883A1
公开(公告)日：2019-11-06
申请号：EP18177566.9
申请日：2018-06-13
发明人： MANAMOHAN, Sathyanarayana , SHASTRY, Krishnaprasad Lingadahalli , PANDEY, Avinash Chandra , SARVESWARA, Ravi
摘要： The disclosure relates to decentralized management of edge nodes operating outside an enterprise network using blockchain technology. A management node may operate within a firewall of the enterprise to manage the edge nodes operating outside the firewall using blockchain technology. The management node may coordinate management by writing change requests to a decentralized ledger. The edge nodes may read the change requests from its local copy of the distributed ledger and implement the change requests. Upon implementation, an edge node may broadcast its status to the blockchain network. The management node may mine the transactions from the edge nodes into the distributed ledger, thereby creating a secure and scalable way to coordinate management and record the current and historical system state. The system also provides the edge nodes with a cryptographically secured, machine-to-machine maintained, single version of truth, enabling them to take globally valid decision based on local data.
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公开(公告)号：EP3564873A1
公开(公告)日：2019-11-06
申请号：EP18183265.0
申请日：2018-07-12
摘要： Decentralized machine learning to build models is performed at nodes where local training datasets are generated. A blockchain platform may be used to coordinate decentralized machine learning over a series of iterations. For each iteration, a distributed ledger may be used to coordinate the nodes. Rules in the form of smart contracts may enforce node participation in an iteration of model building and parameter sharing, as well as provide logic for electing a node that serves as a master node for the iteration. The master node obtains model parameters from the nodes and generates final parameters based on the obtained parameters. The master node may write its state to the distributed ledger indicating that the final parameters are available. Each node, via its copy of the distributed ledger, may discover the master node's state and obtain and apply the final parameters to its local model, thereby learning from other nodes.
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