Abstract: 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.

Abstract: Systems and methods are provided for implementing pattern detection as a first step for security improvements of a computer network. The pattern detection may utilize a machine learning (ML) model for predicting network tuple parameters. The ML model can be trained on labelled data flow information and deployed by a central server for preventing network-wide cyber-security challenges (e.g., including DNS flux, etc.). Networking devices (e.g. switches, etc.) can monitor the data flow traffic that it receives from the networking devices and classify network tuple parameters based on the flow behavior. The system can compare the output of the ML model (e.g., a classification of the data flow traffic, etc.) to an implicit label (e.g., the network tuple parameter included with the data flow traffic, etc.). When the classification matches a particular network tuple parameter, the system can generate an alert and/or otherwise identify potential network intrusions and other abnormalities.

Abstract: A system and a method for training non-parametric Machine Learning (ML) model instances in a collaborative manner is disclosed. A non-parametric ML model instance is trained at each of a plurality of data processing nodes to obtain a plurality of non-parametric ML model instances. Each non-parametric ML model instance developed at each data processing node is shared with each of remaining data processing nodes of the plurality of data processing nodes. Each non-parametric ML model instance is processed through a trainable parametric combinator to generate a composite model at each of the plurality of data processing nodes. The composite model is trained at each of the plurality of data processing nodes, over the respective local dataset, using Swarm learning to obtain trained composite models.

Abstract: Systems and methods are provided for implementing pattern detection as a first step for security improvements of a computer network. The pattern detection may utilize a machine learning (ML) model for predicting network tuple parameters. The ML model can be trained on labelled data flow information and deployed by a central server for preventing network-wide cyber-security challenges (e.g., including DNS flux, etc.). Networking devices (e.g. switches, etc.) can monitor the data flow traffic that it receives from the networking devices and classify network tuple parameters based on the flow behavior. The system can compare the output of the ML model (e.g., a classification of the data flow traffic, etc.) to an implicit label (e.g., the network tuple parameter included with the data flow traffic, etc.). When the classification matches a particular network tuple parameter, the system can generate an alert and/or otherwise identify potential network intrusions and other abnormalities.

Abstract: A system and a method for detecting and defending against adversarial attacks in decentralized learning models are described. The method comprises obtaining a learning parameter for determining a reference cryptographic hash value and a similarity between the data processing nodes (102). A cryptographic hash value is determined for each data processing node (102) based on the learning parameter. The trust score of each data processing node (102) is updated based on matching of the cryptographic hash value with the reference cryptographic hash value. The learning parameter of each data processing node (102) is merged to obtain a merged learning parameter based on the trust score. The merged learning parameter is provided to the data processing nodes (102) to be used for training the machine learning models.

Abstract: Systems and methods are disclosed for providing decentralized policy-based transactional object management for systems employing federated workflows. Various disclosed components may be added to one or more nodes of a decentralized network, wherein the disclosed components perform registration, replication, and read/write access interfacing functions. These functions result in the storage of objects on the decentralized network in a way which allows for decentralized, policy-based, and transactional management of the objects.

Abstract: Anomalies and drift detection in decentralized learning environments. The method includes deploying at a first node, (1) a local unsupervised autoencoder, trained at the first node, along with a local training data reference baseline for the first node, and (2) a global unsupervised autoencoder trained across a plurality of nodes, along with a corresponding global training data reference baseline. Production data at the first node is processed with local and global ML models deployed by a user. At least one of local and global anomaly data regarding anomalous production data or local and global drift data regarding drifting production data is derived based on the local and global training data reference baselines, respectively. At least one of the local anomaly data is compared with the global anomaly data or the local drift data with the global drift data for assessing impact of anomalies/drift on the ML models.

Abstract: 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 (ML) over a series of iterations. For each iteration, a distributed ledger may be used to coordinate the nodes communicating via a blockchain network. A node can include self-healing features to recover from a fault condition within the blockchain network in manner that does not negatively impact the overall learning ability of the decentralized ML system. During self-healing, the node can determine that a local ML state is not consistent with the global ML state and trigger a corrective action to recover the local ML state. Thereafter, the node can generate a blockchain transaction indicating that it is in-sync with the most recent iteration of training, and informing other nodes to reintegrate the node into ML.

Abstract: 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 (ML) over a series of iterations. For each iteration, a distributed ledger may be used to coordinate the nodes communicating via a blockchain network. A node can include self-healing features to recover from a fault condition within the blockchain network in manner that does not negatively impact the overall learning ability of the decentralized ML system. During self-healing, the node can determine that a local ML state is not consistent with the global ML state and trigger a corrective action to recover the local ML state. Thereafter, the node can generate a blockchain transaction indicating that it is in-sync with the most recent iteration of training, and informing other nodes to reintegrate the node into ML.

Abstract: Systems and methods are provided for leveraging blockchain technology in a swarm learning context, where nodes of a blockchain network that contribute data to training a machine learning model using their own local data can be rewarded. In order to conduct such data monetization in a fair and accurate manner, the systems and methods rely on various phases in which Merkle trees are used and corresponding Merkle roots are registered in a blockchain ledger. Moreover, any claims for a reward are challenged by peer nodes before the reward is distributed.

Abstract: 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 (ML) over a series of iterations. For each iteration, a distributed ledger may be used to coordinate the nodes communicating via a blockchain network. A node can include self-healing features to recover from a fault condition within the blockchain network in manner that does not negatively impact the overall learning ability of the decentralized ML system. During self-healing, the node can determine that a local ML state is not consistent with the global ML state and trigger a corrective action to recover the local ML state. Thereafter, the node can generate a blockchain transaction indicating that it is in-sync with the most recent iteration of training, and informing other nodes to reintegrate the node into ML.

Abstract: Systems and methods provide for a federated workflow solution to orchestrate entire machine learning (ML) workflows comprising multiple tasks, across silos. In other words, one or more sets/pluralities of tasks making up an ML workflow, can be executed across multiple resource partitions or domains. Federated workflow state can be maintained and shared through some form of distributed database/ledger, such as a blockchain. Agents that are locally deployed locally at the silos may orchestrate an ML workflow at a particular resource domains, each such agent having access, via the blockchain (acting as a globally visible/consistent state store), to the aforementioned workflow state. Such systems are capable of operating regardless of the existence of heterogeneous resources/aspects of a silo.

Abstract: Systems and methods are provided for implementing swarm learning while using blockchain technology and election/voting mechanisms to ensure data privacy. Nodes may train local instances of a machine learning model using local data, from which parameters are derived or extracted. Those parameters may be encrypted and persisted until a merge leader is elected that can merge the parameters using a public key generated by an external key manager. A decryptor that is not the merge leader can be elected to decrypt the merged parameter using a corresponding private key, and the decrypted merged parameter can then be shared amongst the nodes, and applied to their local models. This process can be repeated until a desired level of learning has been achieved. The public and private keys are never revealed to the same node, and may be permanently discarded after use to further ensure privacy.

Abstract: The disclosure relates to decentralized management of nodes in a blockchain network. Participants may agree to a consensus rules and implement them as smart contracts. For example, one rule may specify that a node will accept a change proposal only when its local policies and/or data allow it to implement the change. A smart contract may implement this rule and deploy it across the blockchain network for each node to follow. Other participants, through their nodes, may propose changes to the blockchain network, and each node may consult its copy of the smart contract to determine whether to vote to approve the change request and apply the change request locally.

Abstract: Systems and methods are provided for leveraging blockchain technology in a swarm learning context, where nodes of a blockchain network that contribute data to training a machine learning model using their own local data can be rewarded. In order to conduct such data monetization in a fair and accurate manner, the systems and methods rely on various phases in which Merkle trees are used and corresponding Merkle roots are registered in a blockchain ledger. Moreover, any claims for a reward are challenged by peer nodes before the reward is distributed.

Abstract: Systems and methods are provided for machine learning in a distributed, privacy-preserving manner. Particularly, the decentralized system can share machine learning models in a protected manner by training a first sub-model with a first local data set at a first node and obfuscating the trained first sub-model as a first obfuscated sub-model. The model may be shared with a second node, that can construct a local instance of a stacked ensemble comprising the first obfuscated sub-model and a trainable parametric layer and train the local instance of the stacked ensemble with a second local data set accessible locally at the second node.

Abstract: Systems and methods are provided for implementing pattern detection as a first step for security improvements of a computer network. The pattern detection may utilize a machine learning (ML) model for predicting network tuple parameters. The ML model can be trained on labelled data flow information and deployed by a central server for preventing network-wide cyber-security challenges (e.g., including DNS flux, etc.). Networking devices (e.g. switches, etc.) can monitor the data flow traffic that it receives from the networking devices and classify network tuple parameters based on the flow behavior. The system can compare the output of the ML model (e.g., a classification of the data flow traffic, etc.) to an implicit label (e.g., the network tuple parameter included with the data flow traffic, etc.). When the classification matches a particular network tuple parameter, the system can generate an alert and/or otherwise identify potential network intrusions and other abnormalities.

Abstract: 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.

Abstract: Systems and methods are provided for leveraging blockchain technology in a swarm learning context, where nodes of a blockchain network that contribute data to training a machine learning model using their own local data can be rewarded. In order to conduct such data monetization in a fair and accurate manner, the systems and methods rely on various phases in which Merkle trees are used and corresponding Merkle roots are registered in a blockchain ledger. Moreover, any claims for a reward are challenged by peer nodes before the reward is distributed.

Abstract: An example device comprising contactless circuitry to receive data about a plurality of events corresponding to an asset, and a memristor coupled to the contactless circuitry to store the data about the plurality of events. The contactless circuitry may determine that the asset has experienced an event, receive a transaction corresponding to the event from a decentralized entity, generate a hash of the transaction including a device identifier of the contactless circuitry and the transaction received from the decentralized entity, verify the hashed transaction with the decentralized entity, and store the verified hashed transaction on the memristor of the contactless circuitry, wherein the stored verified hash includes information about the event.