508 results about "Consensus" patented technology

A distributed indexing and resolution architecture is disclosed for decentralized systems requiring modular, trust-scoped mutation control and dynamic alias governance. The system comprises a plurality of index entries arranged in a parent-child hierarchy, each associated with a structured alias and governed by one or more anchor nodes. Anchors perform localized resolution, mutation validation, and restructuring operations under deterministic policy constraints, enabling semantic scope enforcement and entropy-sensitive adaptation without requiring global consensus or centralized control. The architecture supports scoped alias traversal, asynchronous mutation proposals, and elastic anchor registration based on system state metrics. The indexing substrate may be integrated into heterogeneous infrastructures, including systems comprising distributed software agents, semantic execution platforms, or pseudonymous identity frameworks. Anchors coordinate within defined trust domains to ensure lineage continuity, dynamic rekeying, and semantic integrity across independently governed segments of a decentralized namespace.

The invention discloses a large-model-driven automatic knowledge graph construction method based on a confidence feedback mechanism, and aims to improve the structural accuracy and semantic consistency in a structured triple generation process, and perform structural constraint guidance by using a few-sample prompt mechanism and a cross validation mechanism of a heterogeneous large model. And the control capability of the large language model on the triple format is enhanced, so that format offset and semantic redundancy in the generation process are reduced. And meanwhile, a multi-dimensional confidence evaluation system is constructed, model consensus judgment, semantic rationality analysis and knowledge consistency verification are fused, and refined quantification and screening of triple quality are realized. According to the method, a confidence backtracking feedback strategy is introduced, a generation-verification-optimization closed-loop process is constructed, the expression and correction capability of the system on a complex knowledge structure is enhanced, the dependence on an external API is effectively reduced, the consumption of computing resources is reduced, and the operation efficiency of the system and the feasibility of engineering deployment are remarkably improved.

The invention discloses a vehicle-mounted controller firmware upgrading method, which relates to the technical field of intelligent networked automobiles, and comprises the following steps of: accurately describing a dependency relationship between versions by introducing a directed acyclic graph, providing a clear topological sorting basis for an upgrading process, and improving the upgrading efficiency. The system function conflict and disorder caused by disordered upgrading sequence are fundamentally avoided; a distributed consensus protocol is utilized to ensure that each controller achieves state coordination before upgrading, so that the overall robustness of a multi-controller cluster is effectively improved when the multi-controller cluster faces network delay or individual node abnormity, and upgrading failure or version bifurcation caused by the fact that part of nodes are not ready is avoided; through the combination of the group-level binding token and the atomic switching mechanism, the controller group with a strong function dependency relationship can be ensured to be effective and updated as a unified transaction unit, and the safety and reliability of cross-domain function linkage are remarkably enhanced.

The invention discloses a decentralized multi-agent information interaction method and system, and the method comprises the steps: generating a subtask carrying a unique identifier and metadata based on mixed dimension task decomposition, and embedding a global utility function; sub-task-agent mapping is dynamically adjusted through Kafka and Flink, and a utility function weight coefficient is broadcasted; the sub-agent determines an executor through contract network protocol bidding, and performs execution permission arbitration on key resources in combination with a token bucket algorithm; metadata are transmitted by adopting a Gossip protocol, and intermediate data are transmitted by P2P direct connection (abnormal degradation to Kafka); key metadata are synchronized through lightweight Raft consensus, and non-key metadata are merged by adopting CRDT; and after a final result is written in through Kafka, duplicate removal, sorting and aggregation are carried out through Flink, and key states are synchronized through Gossip. According to the method, the problem of message disorder under a hybrid protocol is solved; and high-reliability message transmission and state synchronization are realized by combining the strong consistency bottom of Kafka and the conflict resolution of lightweight Raft and CRDT.

The invention provides a zero-sum game-based distributed elastic consensus optimal control method under denial of service attack of a multi-agent system, and the method specifically comprises the steps: firstly, constructing a multi-agent formation model in which a leader and a plurality of followers cooperatively move through graph theory knowledge and a multi-agent second-order state equation; secondly, in order to reduce the influence of denial of service attack on communication topology, a time-varying weight distributed elastic observer is provided to estimate the state of a leader, and the attacked condition of the leader is considered; then, by constructing an augmentation system, a distributed consistency tracking problem with a leader is converted into a local tracking problem between each follower and a virtual leader thereof; and finally, in order to solve the zero-sum game problem, introducing a Hamiltonian-Jacobian-Ansaxophone equation to realize optimal control input under maximum external disturbance, and realizing algorithm design by using single-evaluation reinforcement learning with experience playback and combining a gradient descent method.

The invention discloses a manufacturing task autonomous negotiation and execution method based on a large language model agent, and the method comprises the steps: constructing a production scheduling agent, an equipment management agent, a material distribution agent and a quality control agent, analyzing a natural language task instruction through the production scheduling agent, and decomposing the natural language task instruction into subtasks; each agent calculates a utility value based on the load rate, the resource matching degree, the estimated completion time and the historical success rate, and performs structured negotiation to achieve a task allocation consensus; a prediction-check-rollback architecture is adopted to generate an action instruction sequence, the sequence is compiled into a time Petri network transition sequence, and reachability verification is carried out based on hard security constraints; production environment data is collected in real time to trigger anomaly detection and re-negotiation, and a formalized security verification and causal anti-factual reasoning parameter updating mechanism is introduced. According to the method, unstructured instruction understanding, autonomous task planning, multi-agent collaborative decision and closed-loop optimization are realized, and the problems of real-time performance, safety and interpretability of a large language model in manufacturing control are solved.

The invention discloses a heterogeneous computing power resource dynamic scheduling system and method based on multi-objective optimization, and the system is deployed in a digital ecological platform complex based on multivariate consensus and embedded intelligent management. Comprising a platform access module, a data acquisition and perception module, a multi-target modeling and optimization module, a dynamic scheduling and execution module and a fault-tolerant mechanism module. The system registers as a computing power scheduling service node through an intelligent contract interface, obtains and verifies the compliance of a computing task, collects heterogeneous computing power resource node state data, and generates a scheduling decision by dynamically adjusting the weight of a target function; evaluating task suitability by using an AMCU suitability scoring model, and executing a scheduling AMCU strategy; according to the method, the technical problems of low resource scheduling efficiency and poor fault-tolerant capability in a heterogeneous computing power environment are solved, and the system throughput and the resource utilization rate are improved.

The invention discloses a lightweight block chain node consensus mechanism optimization method, and relates to the technical field of block chains. According to the method, the computing power of the lightweight nodes is accurately evaluated through a hierarchical verification aggregation algorithm, efficient participation of the lightweight nodes in a resource-constrained environment is realized, a transaction set is divided into a plurality of subsets by adopting a transaction verification fragmentation strategy, and verification results are aggregated by utilizing a Merkel tree aggregation verification technology; in addition, a dynamic storage management strategy is established, the depth of locally stored historical data is dynamically adjusted according to the node storage capacity, and network storage and retrieval are performed through a distributed hash table; the bottleneck problem of storage and network transmission is effectively solved, and the network bandwidth utilization efficiency and the storage management flexibility are improved.

The invention relates to the field of computer science and technology, and discloses an adaptive consensus method for unmanned aerial vehicle edge calculation. Deploying a permission chain at the edge node and initializing a consensus committee; a reliability index and a response time delay index of each node are maintained through an online learning mechanism; samples are extracted from node posterior distribution based on Thompson sampling, a comprehensive score is calculated in combination with a time delay index, and a consensus committee is dynamically reconstructed through epsilon smoothing and a Gumbel-Top-k algorithm; weighted lot drawing is carried out based on the current committee node weight to determine a main node, and a dual-path view switching mechanism is adopted to guarantee the system activity. The problems of high consensus long tail time delay, poor robustness and privacy disclosure caused by link time varying and node heterogeneity in an unmanned aerial vehicle edge scene are solved, and low-overhead, high-throughput and strong-self-adaption consensus management is realized.

The invention relates to a computer system data backup technology, and discloses a dynamic high-reliability verifiable backup method and system. Multi-dimensional features such as data entropy and access frequency are extracted and classified into key, important and common data through machine learning; the hierarchical backup strategy comprises key data real-time increment, important data dynamic partitioning and common data period full amount, and edge node loads are dispatched in combination with federal learning; pBFT consensus edge cluster distributed storage is adopted, a backup data hash uplink evidence is stored, and the consistency is regularly verified by a smart contract; parallel processing is carried out according to priorities during recovery, and the integrity is verified through on-chain hash. The problems of static strategies, single-point faults, integrity loss and efficiency are solved, the data reliability is improved, the recovery efficiency and compliance are improved, and the method adapts to high-security scenes such as finance and medical treatment.

The invention provides a multi-agent cooperation-oriented process planning and execution system and method, and the system comprises an autonomous process planning engine which dynamically generates and optimizes a process path based on a reinforcement learning model according to a real-time business rule; the multi-Agent collaborative execution layer receives a flow path, performs task decomposition and adaptive load balancing to intelligently distribute sub-tasks to a plurality of Agents for parallel execution, and adopts a consensus protocol to guarantee the consistency of key data; and the fault self-healing guarantee system continuously monitors the running state of the system, identifies a source through an intelligent positioning module when detecting an abnormality, and automatically generates and executes a recovery strategy in combination with a preset template and a dynamic learning mechanism. By constructing an intelligent closed loop of planning, execution and self-healing, dynamic self-adaption, efficient cooperation and high-reliability operation of the business process are achieved, complex business changes and system anomalies can be coped with without manual intervention, and the intelligent level and the business continuity guarantee capability of an automatic system are remarkably improved.

The invention discloses an efficient double-link block chain consensus method and device, equipment and a storage medium, and belongs to the field of information security. The method comprises an initialization stage, an input stage, a coding stage, a broadcasting stage and a consensus stage. According to the invention, the parallel design of a coding broadcast link and a consensus confirmation link is adopted, so that the occurrence frequency of transaction congestion is reduced; the integrity required by the asynchronous Byzantine consensus process is realized through three-step broadcasting, the updating problem of the post-quantum component does not need to be considered, and the security is higher; an asynchronous network and a Byzantine opponent model are adopted, so that malicious node attacks which do not exceed the total number of nodes can be tolerated on the premise of not setting a network time delay upper limit; in the encoding stage, the generation of the redundancy share through the RS erasure code can be realized by adopting preprocessing, the data redundancy expansion multiple in the broadcasting stage is far smaller than that of the same type of protocol, and the original transaction can be recovered by collecting part of effective shares in the consensus stage, so that the system time delay is effectively reduced, and the system throughput is improved.

The invention discloses an AI-driven block chain hybrid consensus dynamic hierarchy optimization method and system, and aims to solve the problems that an existing block chain consensus mechanism is insufficient in adaptability, low in intelligent contract execution efficiency, difficult in security and privacy balance and the like. A dynamic hierarchical adaptive hybrid consensus architecture is constructed, a hierarchical structure is adjusted in real time according to a service load, and super nodes are dynamically elected to process high-frequency transactions; predicting a load by combining a deep reinforcement learning model, and automatically selecting an optimal consensus combination; high-frequency data memory storage and low-frequency data distributed storage are realized through intelligent contract routing, and graphic processor acceleration nodes are allocated based on contract complexity; establishing a node reputation scoring system, verifying a reputation level in combination with zero-knowledge proof, and automatically switching to DAG asynchronous consensus and starting node cleaning when an attack in a specific numerical range is detected; the deep fusion of the hybrid consensus and the dynamic hierarchy is realized, so that the execution efficiency is improved. The high-frequency transaction processing efficiency is improved, and the complex contract efficiency is improved.

The invention discloses an intelligent construction scheme auditing method based on self-adaptive multi-agent cooperation, relates to the technical field of construction scheme auditing, and solves the problems that technical key points cannot be deeply identified, different professional perspectives cannot be cooperated, meanwhile, a supervision layer and an anti-thinking mechanism are lacked, quality monitoring is not available in the auditing process, and the auditing efficiency is high. And errors and logic contradictions may be directly output. Through a dynamic agent generation technology, the system can adaptively establish an agent auditing team according to different scheme characteristics, and a multi-agent cooperation and shared blackboard mechanism is adopted to simulate a multi-expert joint auditing scene; by introducing a consensus decision algorithm based on confidence fusion, the system can autonomously negotiate and judge opinion conflicts among different agents or standard provisions, and output a unified conclusion. Through the meta-agent layer, the system realizes real-time monitoring and reflection of the auditing process of the system, and can automatically check reasoning logic, identify a low-confidence conclusion and trigger secondary auditing.

The invention relates to a practical consensus algorithm for file resource sharing, which comprises reputation-driven task allocation: according to reputation values calculated in real time, nodes with high reputation values are preferentially selected by the algorithm to form a core consensus group to execute the most critical consensus task, and the number of nodes participating in core communication is remarkably reduced. And protocol flow optimization: fundamentally reducing the communication complexity to O (n) level by limiting the scale of the core consensus group and reconstructing a message transmission path. And node state dynamic autonomy: the algorithm has real-time evaluation and dynamic adjustment capabilities on node types. The reputation model continuously monitors node behaviors, can automatically perform degradation processing on low-reputation or fault nodes, and can timely discover and safely isolate or delete bad nodes through a specific mechanism without restarting the system. Through test verification, the reliability, safety, higher throughput and lower delay of the consensus algorithm are verified.

The embodiment of the invention provides a multi-agent decision deduction collaboration method and system based on a large language model. The method comprises the following steps: S1, receiving an input decision demand text, and determining and initializing a plurality of agents in different fields; s2, driving a plurality of agents to perform initial viewpoint expression and at least one round of interactive discussion; wherein in each round of interaction, S21, a natural language viewpoint of the round is generated based on a semantic fusion frame formed by an initial viewpoint or a historical interaction round; s22, fusing the natural language viewpoints generated by the intelligent agents at the message level, and converting the natural language viewpoints into structured message viewpoints; s23, the structured message viewpoint is transmitted among the agents, and the multiple agents are driven to interact, respond and debate to generate interaction viewpoints; and S24, carrying out secondary fusion on the interaction viewpoints formed after the current round of interaction. And S3, performing global level fusion on semantic fusion frames generated by multiple rounds of interaction, and generating a structured deduction result containing a consensus field and a divergence field.

The invention belongs to the technical field of block chains, and particularly relates to a financial and tax data fusion and audit traceability management system based on a block chain, which comprises an under-chain multi-source data acquisition layer, an on-chain dynamic fusion verification layer and a cross-domain collaborative feedback control layer, and is pre-bound to a data source through a business rule engine. Through combination of a lightweight fusion unit of space-time alignment and an incremental consensus mechanism based on a state channel, semantic consistency, fine-grained tracing and real-time compliance intervention of finance and tax data in a full life cycle are realized. Through a business rule pre-binding and source event capturing mechanism, finance and taxation data is prevented from breaking away from compliance context in a generation stage; a space-time alignment fusion unit is utilized to construct a composite transaction object with causal logic, and semantic segmentation among multi-source data is eliminated; the incremental consensus architecture based on the state channel is realized, and the final consistency of key transactions is ensured while the high throughput performance is maintained.

A method and system for executing chained instructions in a resilient, decentralized architecture are disclosed. The system enables complex workflows through an asynchronous continuation mechanism. An initial instruction, executed by a first entity in a leading validation cluster, can generate one or more continuation instructions directed to other entities. These continuations are executed immediately on a “fast track” path within the leading cluster, creating a high-speed, non-blocking sequence of cross-entity actions prior to network-wide consensus. The resulting ordered sequence is then proposed to follower clusters, which perform a consensus before executing a duplicate, verifiable instance of the entire instruction chain. This separation of optimistic, continuation-driven execution from deliberate, consensus-based finality facilitates a pipelined and parallel processing model. The architecture provides the foundation for resilient microservice applications requiring complex, low-latency, and asynchronous interactions across a scalable, decentralized system.

The invention provides a large-scale complex group consensus decision-making method, terminal equipment and a medium. The method comprises the following steps: acquiring a probability language evaluation matrix of a plurality of agents participating in decision-making on a plurality of candidate schemes on a plurality of scheme attributes; adjusting the probabilistic language evaluation matrix based on a reinforcement learning algorithm, and driving a plurality of agents to reach a consensus to obtain a new probabilistic language evaluation matrix; for the new probabilistic language evaluation matrix, utilizing a Bayesian hierarchical model to calculate an attribute weight corresponding to each scheme attribute; according to the hesitation degree and the consensus degree of each agent in the new probability language decision matrix, determining an agent weight corresponding to the agent; converting the new probability language evaluation matrix into a numerical evaluation matrix through a cloud model; fusing the numerical evaluation matrix, the attribute weight and the agent weight to obtain a scheme evaluation cloud model; and determining a final scheme for achieving the large-scale complex group consensus from the plurality of candidate schemes according to the scheme evaluation cloud model. According to the invention, the precision of large-scale complex group consensus decision can be improved.

A system and method may generate a normalized protocol model that leverages heterogeneous distributed ledger technology and is interoperable across different blockchain networks that may initially use different network protocols. The model may be generated by leveraging neuro-symbolic artificial intelligence (AI). The model that is generated may be based on the different network protocols that are in use at the different blockchain networks. The model may be adopted by the blockchain networks upon the blockchain networks providing unified consent to its adoption. The model may be required to be implemented by each of the different blockchain networks. The consensus to adopt the model may be recorded in one or more smart contracts. Copies of the smart contracts may be stored at the blockchain networks. The normalized protocol model may allow electronic transaction, data exchanges, and communications to be performed across blockchain networks and recorded in a heterogeneous distributed ledger.

Federated gradient consensus framework for secure and trust-adaptive collaborative model training across distributed nodes, including: multiple client nodes that perform local training and gradient computation; a federated gradient consensus layer that validates gradient directions and forms a distributed consensus; a confidence rating unit that assigns dynamic confidence weights to the nodes; and a secure gradient aggregator that uses cryptographic or multi-party computational methods for privacy-compliant aggregation.

The invention discloses a block chain dynamic authority management system and method. The system comprises a dynamic entropy generation module, an entropy fusion and mapping module and a closed loop execution module. The dynamic entropy value generation module combines position entropy, behavior information entropy and node distribution divergence to realize multi-dimensional information perception; the entropy fusion and mapping module calculates an entropy value based on a space-time entropy function, and performs permission level mapping and consensus weight distribution according to the entropy value; and the closed-loop execution module implements permission strategy reconstruction and migration through a space tearing and gene recombination mechanism when nodes are abnormally distributed or entropy values are abnormal, so that the dynamic safety and the self-adaptive capability of the system are guaranteed. According to the invention, high-credibility and flexible control of the equipment authority in the distributed environment can be realized, and the security and intelligence of the block chain system are improved.

Decentralized information-security (IS) mitigates against cross-channel threats vectors with a photonic quantum computing machine (PQCM). Threat signals are communicated to consortium members. Live model libraries regarding the threats, characteristics, metadata, model solutions, etc. are in distributed ledgers. PQCM extracts metadata and analyzes permutations to identify configuration(s) with the highest propensity to mitigate the threat. PQCM determines optimized set(s) of the threat-vector mitigation models for the configuration(s). PQCM auto-generates, dynamically by AI / ML based on the optimized set, IS rules for the configuration. Updated threats, threat characteristics, model configurations, IS rules, etc. for the cross-channel threat can be stored in distributed ledger blockchains, shared with consortium members, and deployed to prevent the threat. Nodal consensus algorithms may be used to reach agreement amongst consortium members to independently confirm threat signal validity, best model combinations, highest propensity scores, and proposed dynamically generated IS rules to address the threat.

The embodiment of the invention discloses an alliance chain fragment parameter dynamic optimization method based on deep reinforcement learning, and the method comprises the steps: building a deep reinforcement learning model-based reward function of an alliance chain facing a government affair alliance chain system based on an alliance chain constraint condition facing the government affair alliance chain system; in combination with a state space and an action space which are based on a deep reinforcement learning model and face an alliance chain of a government affair alliance chain system, a high-efficiency fragmentation block chain performance optimization model based on the deep reinforcement learning model is adopted to obtain a fragment number, a block size, a block interval and a full-node proportion for fragmenting the alliance chain; therefore, dynamic optimization of the alliance chain fragment parameters is realized. According to the method, the award function is established in combination with the security constraint, so that award high throughput and storage optimization are realized, and meanwhile, the fragmentation and consensus fusion degree can be greatly improved in a cross-fragment transaction frequent scene.

A symbolic execution kernel for artificial general intelligence (AGI) and artificial superintelligence (ASI) systems is disclosed. The kernel comprises a cognitive logic module for constraint-based symbolic instruction execution, a cryptographic arbitration engine for ethical branch verification, and a runtime firewall for threat detection and symbolic graph mutation neutralization. Symbolic instructions are processed as constraint-satisfaction problems verified by satisfiability modulo theory solvers and cryptographically sealed for integrity. Behavioral sequences are preserved using Merkle hash trees, and multimodal inputs including electroencephalography signals undergo symbolic verification. Zero-knowledge proofs, dual-kernel consensus, and rollback logic provide resilience against faults and ethical drift. The architecture achieves arbitration within five microseconds and ensures lawful and deterministic execution under hardware or network compromise.

The invention provides a code security detection and version control method and system oriented to a distributed environment, and relates to the technical field of software security, and the method comprises the following steps: receiving a source code file submitted by a distributed node, analyzing a syntax tree structure, extracting semantic features and control flow features, determining a code calling relationship and a data access mode, and storing the code calling relationship and the data access mode; establishing a cross-node code dependency graph; tracking a calling link and a data flow direction on the dependency graph, calculating calling frequency, access authority and data interaction strength among nodes, and identifying a security risk propagation path; constructing a federated learning network training security risk identification sub-model, and generating a security level evaluation result through encryption gradient aggregation and a differential privacy protection mechanism; double confirmation is carried out through Hash verification of a proof-of-workload layer and threshold verification of a voting consensus layer, and version state conversion is constrained based on a time sequence dependency chain. According to the method, the security risk of the distributed code can be accurately identified, and the secure and controllable management of the version is realized.

The invention discloses a quantum key distribution system and method. According to the system, a phase coherence monitoring mechanism is innovatively introduced, and early attack detection is realized through a dynamic consensus sampling protocol; a zero-trust relay network is constructed by adopting a wavefront fragmentation principle, and trust dependence on relay nodes is eliminated; self-adaptive safety monitoring is realized based on a finite state automaton, and a system safety strategy is dynamically adjusted. According to the method, the problems of resource consumption type attack, relay trust dependence, static protocol limitation and the like faced by an existing QKD system are solved through a strict mathematical framework, and long-distance quantum key distribution with efficient resources, endogenous security and elasticity and physical layer security guarantee is realized. Experiments show that compared with a traditional scheme, the detection delay of the system is reduced by about 38%, the resource waste is reduced by about 40%, and the availability of the system reaches 99.995% or above.

The invention discloses a multi-agent consistency tracking control method based on an observer, and belongs to the technical field of multi-agent systems, and the method comprises the steps: constructing a second-order leader following multi-agent dynamic model with external disturbance; determining a network communication topology structure of the multi-agent system; constructing a fixed time tracking error observer; establishing a dynamic event triggering protocol; establishing a consistency tracking control protocol according to the output of the observer and based on a backstepping method; and enabling each multi-agent to communicate according to the communication topology, when a trigger condition is met, the agent sends own state information to a neighbor, the observer updates output information according to the state information, and the controller outputs the output information to the actuator at the same time. According to the invention, the anti-interference capability of the system can be improved, and the consumption of communication resources and energy of the system is reduced.

The invention discloses an industrial Internet of Things cooperative management method and system of a double-track block chain consensus engine, computer equipment and a medium, and the method comprises the steps: carrying out the data distribution based on multi-type data, and carrying out the dynamic fragmentation of high-priority real-time data based on a core layer; sorting the blocks in each fragment, executing a dual-stage submission protocol, and outputting an encrypted block; tracking behavior data of the nodes in real time based on a daemon layer, performing anomaly detection, and synchronizing an anomaly evidence set to all the nodes based on a Byzantine broadcast mechanism when an abnormal event is detected; and performing hash chain verification on the encrypted block, transmitting the encrypted block passing the verification into a trusted execution environment, and issuing an industrial control instruction to a production line execution terminal after verifying a hardware signature. According to the invention, low-delay consensus, rapid fault recovery, cross-platform compatibility and dynamic fault-tolerant balance can be realized through a double-track collaborative architecture of the core layer and the guard layer in combination with the collaborative verification capability of the trusted execution environment.

Systems and methods are provided for using Connected Dominating Sets (CDS) in a Distributed Average Consensus (DAC) process to bridge the gap between a fully centralized consensus approach and a fully distributed one. Embodiments of the present disclosure provide a process for distributed network consensus using network node proxies based on CDS, demonstrating reductions in both the network-wide convergence time and required network communications to achieve agreement. In an embodiment, CDS are used to act as a subgraph of proxy agents for the network iterative communication and computation process, resulting in a network wide DAC result. Embodiments of the present disclosure reduce the required messages and overall delay needed to converge to a distributed group solution.