53 results about "Decentralized computing" patented technology

Systems and methods for decentralized orchestration of event records within decentralized computing environments. A processing system including decentralized execution nodes dynamically allocates microservice instances based upon real-time node performance metrics. Transaction requests including transaction data digitally associated with decentralized identifiers (DIDs) are processed by allocated microservice instances. The processing includes verifying transactions, generating cryptographically-linked immutable memorialization records associated with respective DIDs, and distributing these records to a tamper-evident distributed ledger configured to enforce immutability through consensus nodes. The processing system dynamically reallocates microservice instances among decentralized execution nodes based upon monitored utilization metrics and predefined scaling thresholds. Each memorialization record is cryptographically secured and linked permanently to the DID for data immutability, enhanced security, and dynamic scalability for event data processing in distributed infrastructures.

A computer based system and method for providing decentralized training of a machine learning model, including: obtaining a mini-task of training the machine learning model wherein the mini-task comprises at least one mini-batch size; obtaining an estimation of at least one performance indicator in each trainer of a plurality of trainers that have available computing power, wherein each trainer comprises one or more processors; calculating a maximal micro-batch size for each processor of the plurality of trainers based on the at least one performance indicator of the respective trainer, and parameters of the mini-task; and assigning the mini-task to at least one designated trainer of the plurality of trainers based on the maximal micro-batch sizes of the one or more processors of the at least one designated trainer and the at least one mini-batch size.

Federated Learning systems and methods optimize blockchain transaction processing speed (TPS) and carbon efficiency. Blockchain networks have nodes with a Local Federated Learning Model (LFLM) that can develop / refine computational strategies for block generation in PoW systems or transaction validation in POS systems without revealing sensitive data. Nodes communicate with a Federated Learning Information Server (FLIS) to aggregate, analyze, and distribute optimized strategies across the network to reduce block generation and transaction validation computational resources. This boosts TPS and cuts blockchain network energy use. Incentive-mechanisms reward nodes that create / share efficient computational strategies, encouraging innovation. Dynamic strategy adoption modules select and apply optimum strategies based on network conditions and node capabilities. The POS system auto-approves validators to ensure transaction validation. Decentralized computational strategy exchange via a peer-to-peer protocol is also disclosed. Federated Learning with blockchain technology improves operational efficiency and reduces energy consumption and carbon footprint.

A system and method for providing a randomness function to a smart contact on a decentralized network. An oracle receives a request for at least one random value to be supplied to a smart contract executing on a decentralized network, the request including a seed. The oracle generates the at least one random value based on the seed, and a secret key associated with the oracle, wherein the oracle is identified on the decentralized network by a public key associated with the secret key. The oracle generates a cryptographic proof based on the at least one random value and the public key and sends the at least one random value and the cryptographic proof to the decentralized network, whereby the proof, and thus the randomness of the at least one random value, can be verified.

A Decentralized Trusted Control Plan (D-TCP) to provide a means to define object behaviors without the need for coding on a decentralized computing network. The D-TCP implements pluggable logic through a multitude of decentralized registries with pre-defined code snippets stored on a decentralized network. The D-TCP links the applicable code selected from the registries to create desired functionality while maintaining platform security and providing economic incentives for fostering growth of the multitude of decentralized registries.

A non-fungible token (NFT) platform operates by: receiving character attribute selection data to be associated with a game character that indicates a subset of a set of character attributes of a character attribute database; generating, based on the character attribute selection data, display data associated with the game character; generating game character data that includes the display data and performance data to be associated with actions of the game character in a game and a persona associated with the game character; facilitating generation of a player token NFT associated with the game character, wherein the player token NFT is stored on a blockchain via a decentralized computer system; facilitating play of the game utilizing the game character;10 receiving update data associated with the game character responsive to the play of the game utilizing the game character; and updating the player token NFT based on the update data.

Decentralized computer systems and methods are disclosed for establishing and conducting payment processing with loyalty points using distributed ledger. One method includes: receiving a consumer identification, an issuer identification, and transaction information from a point of sale system when the loyalty card is presented for a transaction of a good or service at a merchant, identifying the network node containing distributed ledger associated with the issuer according to received issuer identification prompting, to participants of the shared ledger, to validate whether the consumer distributed ledger has required loyalty points for the transaction; if all of the participants of the shared ledger accepts the transaction, generating, a new immutable entry to the distributed ledger for the transaction with remaining loyalty points in the loyalty card after deducting the points required for the transaction; and sending a transaction approved message to the point of sale system at the merchant.

A computing node in a P2P computer network obtaining a media item to be verified, determining a hash value for the media item based on a digital cryptographic hash function, retrieving a plurality of data records associated with the media item from a blockchain based at least in part on the hash value, wherein a data record is generated by a validation node in the decentralized P2P computer network, evaluating the plurality of data records to determine whether the media item has satisfied pre-defined validation criteria, and providing information describing the media item based at least in part on the plurality of data records, wherein the information provides an indication as to whether the media item satisfied the pre-defined validation criteria.

Disclosed is system for fulfilling service request using computing nodes. The system comprises a decentralized computing network, comprising computing nodes operating as autonomous agents, configured to implement software framework comprising: client-agent device 5 configured to: receive service request and generate objective therefor, and send objective to agent-device configured to send queries to a machine learning model agent and / or obtain tasks, and list of autonomous agents associated with objective; and processing arrangement configured to: generate directed acyclic graph (DAG) having 10 nodes representing first task and edges representing dependency of first task on second task(s) amongst tasks, and initialize Bloom filter of each node to track completion status of second task(s). Autonomous agent is configured to: identify second task(s), check completion status of second task(s), using Bloom filter of first task, upon completion of second 15 task(s), execute first task, and update Bloom filter.

Decentralized computer systems and methods are disclosed for efficient transaction dispute management using blockchain. One method includes: receiving transaction information of a transaction, wherein the transaction information includes (1) an identifier of an issuer of a payment source account, (2) an identifier of an acquirer of a payment account of a merchant, and (3) the amount of resources to be applied in the transaction; generating a blockchain entry that stores the received transaction information in a shared ledger to participants of the blockchain; receiving in the shared ledger, from a participant, (1) an indication of a dispute of the transaction information, and (2) a proposed change to the transaction information; prompting the participants of the blockchain to respond to the indication of the dispute; if one of the participants denies the proposed change, executing a dispute settlement process to determine a revised transaction information.

A method and apparatus for secured, peer-to-peer transfer of data rights over a computer network, the method being accomplished by a decentralized computing system including a distributed ledger platform and an off-chain data host platform. A data owner authorizes the minting and distribution of an on-chain authorization token using the distributed ledger. The authorization token includes primary and secondary encryption levels to ensure secure data transfer. The authorization tokens are used to track data access rights, enforce access policies, and control distribution of encryption keys transferred between the data owner, an administrator, a recipient, and / or a delegate.

A system and method in which a network of offchain oracle nodes are used to provide dynamic information to consuming smart contracts in a decentralized computing environment. The off-chain oracle network and a corresponding protocol reduces the likelihood of damage from rogue oracles acting in concert. Periodically, the oracle nodes, jointly and in concert, run the protocol to sign a report containing observations from many of the oracles in the network. The report is transmitted to a smart contract C running on a DLT. The smart contract C can validate the report, pay each oracle node that contributed an observation to the report and expose the median, or other calculated value, of the reported values to the on-chain consuming smart contracts.

The present invention discloses a decentralized resource perception and fusion method, apparatus, medium, equipment and product in the field of decentralized computing technology, aiming to solve the problems of limited perception range, weak perception capability and poor fusion of current technologies. The method comprises: performing resource modeling based on the computing resources, storage resources and communication resources of network heterogeneous computing devices to obtain a resource model; performing task modeling using a directed acyclic graph based on the basic attributes of network separable tasks, the dependencies between tasks and the execution constraints of tasks to obtain a task model; adopting a bottom-up clustering fusion method to dynamically perceive and adjust the usage status of resources in the resource model; adopting a local multi-hop fusion method to dynamically perceive during the task execution process based on the task requirements in the task model and the usage status of the resources, and to fuse the perceived resources. The present invention solves the problems of information fragmentation and redundancy in decentralized resources and improves the processing efficiency of tasks.

The present invention proposes an artificial intelligence model training method based on blockchain, which utilizes blockchain technology to ensure data security and the transparency of the training process. Through smart contracts, training data is automatically collected, encrypted, and stored to ensure the privacy and integrity of the data; the system automatically verifies data access permissions according to blockchain rules to ensure that only authorized computing nodes can access the data; the verified data is transmitted to distributed computing nodes through the blockchain, and decentralized computing resources are utilized for model training, and the training results are recorded in real time to ensure the transparency and immutability of the process; blockchain nodes monitor the training process through a consensus mechanism and optimize training parameters to ensure training stability; finally, the model is stored through the blockchain, and the version is released through a smart contract to ensure the traceability and transparency of model updates. This method gives full play to the decentralized characteristics of blockchain, improving the security, efficiency, and credibility of artificial intelligence training.

The invention provides a satellite-ground convergence network decentralized controller allocation method for decentralized time delay calculation, and the method comprises the steps: obtaining a target reward function through constructing a time delay model and a load model, taking the optimization of the target reward function as a training target, and carrying out the decentralized controller allocation of a satellite-ground convergence communication network based on the environment state data in the satellite-ground convergence communication network. Continuously selecting a first current execution action from a preset action space, and calculating a corresponding first target reward function value until the first target reward function value is optimal, taking a corresponding first current execution action as a target coordinate of a network decentralized controller and a target link relationship between the network decentralized controller and a decentralized network computing node, and enabling the time delay and the load to be optimal under the condition that the first target reward function value is optimal; the time delay and load of the satellite-ground converged communication network are both optimal by deploying the network decentralized controller, the time delay of the satellite-ground converged communication network is reduced, and the stability of the satellite-ground converged communication network is improved.

The invention relates to a method of encrypted communication. An encrypted connection to a metadata node is established by a subscriber device via two primary router nodes and one secondary router node of a decentralized computing system. A stream of data packets is divided into corresponding parts between the primary router nodes, and the length of each packet transmitted is shorter than the encryption time. At each of the primary router nodes, the IP address of the transmitting subscriber device is substituted by a primary temporary IP address during the routing of the data packets. At the secondary router node, the primary temporary IP address is substituted by a secondary temporary IP address. The metadata node is the closest accessible metadata node to the secondary router node. At the metadata node, a task is divided into sub-tasks, at least two executing nodes are allocated to each sub-task, data relating to the task is received from the transmitting subscriber device, the received data are distributed among the sub-tasks, and the encrypted portion of the distributed data is sent to each of the executing nodes according to the allocation of the sub-tasks. The technical result consists in improving data protection in a distributed data transmission network.

The invention discloses a federated learning-based satellite internet model security data sharing method, which comprises the following steps of: firstly, constructing a self-adaptive ground mobile terminal clustering strategy based on a dynamic graph attention mechanism, and establishing a space-time sensing ring topology by analyzing a node movement rule and communication characteristics to lay a network foundation for decentration calculation; secondly, a modular decomposition confusion encryption scheme is developed, homomorphic encryption of gradient parameters is realized by using a redundant modular component superposition and random number injection technology, the ciphertext size is compressed, and dependence of a third-party center is avoided through distributed private key management. And finally, creating a multi-sub-ring parallel computing architecture, splitting a modular decomposition task into independent computing units, and realizing local model combination based on a proximity communication principle, so that the bottleneck of a traditional server is eliminated, and the aggregation delay is also reduced. According to the whole scheme, through collaborative design of a topology layer, an encryption layer and a calculation layer, feasibility and efficiency balance of cross-domain collaborative training is achieved under the condition that data privacy is guaranteed.

Approaches are described for a decentralized computing platform that enables dynamic execution of distributed services through a hybrid on-chain and off-chain architecture. The platform consists of a network of nodes, each running a virtual machine to host and execute service instances. A sequencer component records, sequences, and notarizes function calls to maintain a consistent service-level state across the network. Computation takes place off-chain on nodes that participate dynamically in specific services based on resource availability. A persistent virtual memory structure manages both service-level and instance-level state. The service-level state is synchronized across nodes participating in the same service, while the instance-level state is isolated to individual nodes for independent operations. The memory system is non-volatile, retaining state across function calls and enabling direct execution from memory. A universal procedure call (UPC) framework facilitates decentralized communication through multicast-enabled operations with customizable fault tolerance and supports applications from finance to high-performance computing.

A method of operating a blockchain system with decentralized computing nodes includes determining an elected clan representing a. subset of the plurality of the decentralized computing nodes that are capable of approving addition of the block to the blockchain with less than or equal to a threshold probability of failure and performing a block approval process which includes determining a vertex comprising a digest of the block, communicating the vertex to each computing node, communicating the block only to the nodes in the elected, clan, while preventing communication of the block to the nodes that are not members of the elected clan, and receiving one or more messages indicating receipt of the vertex and / or receipt of both the vertex and the block.

Methods and systems for blockchain and smart contract-supported decentralized computation are provided. A computation task from a task initiator node is registered on a blockchain by invoking a smart contract, and assigned to an edge computing node within a decentralized computing network, with task details sent by the task initiator node to the edge computing node through a secure peer-to-peer connection. The task initiator node and the edge computing node may select each other using peer discovery methods based on reputation scores. The edge computing node determines a solution to the computation task. The solution may be verified on-chain by the smart contract, or off-chain by the task initiator node, and a token reward may be given to the edge computing node by the smart contract upon solution verification. The edge computing node is incentivized to determine the solution by the reward provided by the smart contract.

Decentralized computer systems and methods are disclosed for hyperledger-based payment transactions, alerts, and dispute settlement, using smart contracts. One method includes: receiving transaction information comprising transaction data of transaction attributes for a transaction for a good or service originating at a merchant; storing the transaction data of the transaction attributes for the transaction, in a Smart Contract generated or updated by a Smart Contract application; deploying the Smart Contract into one or more blockchain networks, wherein at least one of the one or more blockchain networks; performing one or more iterations of enabling, using an inter ledger protocol, to transfer at least some of the transaction data of the transaction attributes of the transaction to another of the one or more blockchain networks, wherein each of the one or more blockchain networks serves one or more business functions of the transaction.

In some aspects, a computing system may authenticate a user before blockchain functions may be performed and provide obstacles to prevent brute force attacks in decentralized computing systems or other distributed computing system environments. The computing system may use a smart contract to assist with authenticating a user. As authentication attempts fail, the computing system may increase a network usage cost associated with authentication. By doing so, the security of a computing system may be improved by making it cost-prohibitive for malicious actors to perform brute force attacks. Further, a computing system may use a variety of biometric information that may provide additional verification and security when authentication is performed.

A decentralized scheduler node for an unstable network of heterogenous nodes collects status information including a resource capacity of each of a network of nodes with which it is in communication. For each current computation job in its input queue, the scheduler node determines which connected nodes have suitable resource capacity to execute the current job and a reliability of each suitable node based on statistical evaluation of its historical availability for job deployment. From the set of suitably powerful and reliable candidate nodes, the scheduler node selects a candidate node of sufficient resource capacity for deployment of the current job based on the probability of a subsequent job appearing during the execution time of the current job, the subsequent job having an equivalent or greater resource requirement and thus at risk of waiting in the input queue if the selected node is required for executing the subsequent job.

Recovering a private key for a decentralized application transaction is described. Reset information corresponding to a user is sent. An access token that corresponds to the user is received. The access token is sent. A credential to enable the first computing environment to access a third party security service to decrypt encrypted private key information is received. A request for the encrypted private key information is sent. The encrypted private key information is received. The credential and the encrypted private key information is sent. The decrypted private key information is received.

A client device operates by: reading, via the client device, an optical code associated with a player token; sending, via the client device and in response to reading the optical code, a request to download a player token NFT associated with the player token from an NFT distribution platform implemented as a decentralized computer system having a plurality of different nodes, the player token NFT stored on a blockchain via the plurality of different nodes of the decentralized computer system; receiving from the blockchain of the NFT distribution platform, via the client device, the player token NFT associated with the player token responsive to geographically distinct blockchain calculations performed contemporaneously over the plurality of different nodes of a decentralized computer system; and storing the player token NFT in a wallet associated with the user.

Disclosed are systems and methods for a hybrid cloud-edge computing platform using decentralized networks for solving resource-intensive computation tasks such as machine learning and video processing tasks. In some respects, the cloud-edge computing platform comprises a coordinator cluster and worker nodes partitioned into shards, which include cloud-based networks and edge networks, and which cater to a wide range of computation requirements. The coordinator cluster manages workloads by selecting the appropriate shard and worker nodes to execute tasks based on computed efficiency scores. Worker nodes may be incentivized with rewards such as cryptocurrency tokens for successful completion of tasks. The disclosed architecture also supports the pipelining of multiple tasks in a workload, where the output of one task is used as input for another task. In some respects, the computing architecture includes a blockchain-supported edge network, which enables verifying compute tasks and managing reward distribution for worker nodes using smart contracts.

To provide a decentralized processing system, method and storage medium that are accompanied by decentralization of computation tasks to one or more untrusted worker computer systems.SOLUTION: In a method, when an untrusted worker computer system executes calculation for a request computer system, the request computer system (or another verification request computer system) provides information enabling the request computer system to cryptographically verify that the task is completed correctly, the worker computer system provides information including proof and I / O data after the calculation is completed, and the request computer system uses setting, proof and I / O data of open verification key parameters to verify that the calculation executed by the untrusted worker computer system is correct, the calculation executed by a worker being related to a block chain transactions.SELECTED DRAWING: Figure 2

Approaches are described for a decentralized computing platform that enables dynamic execution of distributed services through a hybrid on-chain and off-chain architecture. The platform consists of a network of nodes, each running a virtual machine to host and execute service instances. A sequencer component records, sequences, and notarizes function calls to maintain a consistent service-level state across the network. Computation takes place off-chain on nodes that participate dynamically in specific services based on resource availability. A persistent virtual memory structure manages both service-level and instance-level state. The service-level state is synchronized across nodes participating in the same service, while the instance-level state is isolated to individual nodes for independent operations. The memory system is non-volatile, retaining state across function calls and enabling direct execution from memory. A universal procedure call (UPC) framework facilitates decentralized communication through multicast-enabled operations with customizable fault tolerance and supports applications from finance to high-performance computing.

A physical multi-component article with cross-component deterministic values and underlying value protection (in some cases without any on-board source of power or computing capabilities) is configured to maintain access information for a digital wallet on a decentralized computing platform, like a blockchain-based decentralized computing platform. Private access information, like private key ciphertext and key or passphrase by which the private key ciphertext may be converted to plaintext, are physically concealed with tamper-evident components such that a user can readily determine by visual inspection of the apparatus whether, and what, private access information was divulged. In some examples, a user is required to activate one or more tamper-evident features, thereby altering a visible state of the apparatus, to access certain functions of a digital wallet corresponding to the apparatus.

Distributed denial of service (DDoS) attacks launched using internet of things (IoT) devices may be detected and addressed using decentralized computing, potentially in combination with blockchain or other decentralized ledger technology. One method of doing this may include identifying an anomaly by comparing traffic data against baseline traffic data, reporting an anomaly as a potential attack, receiving an indication that the anomaly is an attack, and then performing a set of response measures. The set of response measures may comprise allocating a dedicated frequency channel to traffic associated with the attack and assigning devices associated with the attack to that channel, while assigning other user devices to different frequency channels. Records of the attack and associated devices may be recorded and propagated across nodes, thereby enabling each node to respond appropriately even as the device moves from its original location. Machine learning can be implemented to improve the analysis of key performance indicators to determine which indicators are most predictive of DDoS attacks.