Abstract: Using a hardware processor, load a matrix. Compute, using the hardware processor, diagonal entry approximations for the matrix by using one or more probing vectors. Apply, using the hardware processor, an Aitken extrapolation to the diagonal entry approximations. Obtain, using the hardware processor, a final diagonal estimation based on the Aitken extrapolation. Optionally, the matrix comprises a matrix of material properties of a first material, and further actions include, based on the final diagonal estimation for the first material, determining that the first material is suitable for a certain application, based on the determination that the first material is suitable, specifying the first material; and, responsive to the specifying, using the hardware processor to control a machine tool to fabricate a part of the first material.

Abstract: Methods and systems for execution of distributed analytics include building a global linked structure that describes correspondences between dataset metadata structures, analytics metadata structures, and location metadata structures and that encodes compatibility between respective datasets, analytics, and locations. A set of analytics and compatible datasets for execution is determined based on the dataset metadata structures, analytics metadata structures, and global linked structure. An optimal execution location is determined based on the determined set of analytics and compatible datasets, the location metadata structures, and the global linked structure. The set of analytics and compatible datasets are deployed to the optimal location for execution.

Abstract: A data mining method, system, and non-transitory computer readable medium include obtaining a subset of public records of data in a public domain and performing data mining, via private domain data, within the subset of the public records of data to find data in the public domain corresponding to a particular individual.

Abstract: A method, a computer program product, and a system of personalized training a machine learning model using federated learning with gradient boosted trees. The method includes training a global machine learning model using federated learning between a plurality of parties. The method also includes distributing the global machine learning model to each of the parties and receiving personalized model updates from each of the parties. The personalized model updates are generated from updated models boosted locally and produced by each of the parties using their respective local data. The method further includes fusing the personalized model updates to produce a boosted decision tree to update the global machine learning model. The method also includes training global machine learning model, iteratively, in this manner until a stopping criterion is achieved.

Abstract: A computer-implemented method, according to one approach, includes issuing a hyperparameter optimization (HPO) query to a plurality of computing devices. HPO results are received from the plurality of computing devices, and the HPO results include a set of hyperparameter (HP)/rank value pairs. The method further includes computing, based on the set of HP/rank value pairs, a global set of HPs from the HPO results for federated learning (FL) training. An indication of the global set of HPs is output to the plurality of computing devices. A computer program product, according to another approach, includes a computer readable storage medium having program instructions embodied therewith. The program instructions are readable and/or executable by a computer to cause the computer to perform the foregoing method.

Abstract: Methods are provided. A method includes announcing to a network meta information describing each of a plurality of distributed data sources. The method further includes propagating the meta information amongst routing elements in the network. The method also includes inserting into the network a description of distributed datasets that match a set of requirements of the analytics task. The method additionally includes delivering, by the routing elements, a copy of the analytics task to locations of respective ones of the plurality of distributed data sources that include the distributed datasets that match the set of requirements of the analytics task.

Abstract: Tensor comparison across a network by determining a first parameter of a first vector representation of a first tensor object associated with a first processor, receiving a second parameter associated with a second vector representation of a second tensor object associated with a second processor, determining a first difference between the first parameter and the second parameter, and sending the first vector representation from the first processor to the second processor, according to the first difference.

Abstract: A method for targeted advertisement includes transmitting a pre-filter to the user device, responsive to contextual information from a user device, to determine, using a processor, one or more inferences based on physical browsing information, collected at the user device, in compliance with one or more privacy policies of the user. The method also includes receiving one or more inferences determined by the pre-filter from the user device and transmitting one or more targeted advertisements to the user device based on one or more inferences.

Abstract: A computer-implemented method, a computer program product, and a computer system for automatic adaptive client selection in federated learning. A server sends parameters of a machine learning model to all of clients, where all of the clients compute respective gradients using the parameters. The server receives sketches of the respective gradients, where the sketches are computed by all of the clients. The server uses the sketches to compute similarity between all of the clients and clusters the all of the clients based on the similarity. The server optimizes a number of client clusters and a dimension of the sketches, subject to a constraint of memory consumption, a constraint of communication overhead, and a performance metric. The server determines a subset of the clients that send the respective gradients, by selecting the clients from the client clusters. The server aggregates the respective gradients sent by the subset of the clients.

Abstract: A method for targeted advertisement includes transmitting a pre-filter to the user device, responsive to contextual information from a user device, to determine, using a processor, one or more inferences based on physical browsing information, collected at the user device, in compliance with one or more privacy policies of the user. The method also includes receiving one or more inferences determined by the pre-filter from the user device and transmitting one or more targeted advertisements to the user device based on one or more inferences.

Abstract: A training process of a machine learning model is executed at the edge node for a number of iterations to generate a model parameter based at least in part on a local dataset and a global model parameter. A resource parameter set indicative of resources available at the edge node is estimated. The model parameter and the resource parameter set are sent to a synchronization node. Updates to the global model parameter and the number of iterations are received from the synchronization node based at least in part on the model parameter and the resource parameter set of edge nodes. The training process of the machine learning model is repeated at the edge node to determine an update to the model parameter based at least in part on the local dataset and updates to the global model parameter and the number of iterations from the synchronization node.

Abstract: A method, a structure, and a computer system for predicting pipeline training requirements. The exemplary embodiments may include receiving one or more worker node features from one or more worker nodes, extracting one or more pipeline features from one or more pipelines to be trained, and extracting one or more dataset features from one or more datasets used to train the one or more pipelines. The exemplary embodiments may further include predicting an amount of one or more resources required for each of the one or more worker nodes to train the one or more pipelines using the one or more datasets based on one or more models that correlate the one or more worker node features, one or more pipeline features, and one or more dataset features with the one or more resources. Lastly, the exemplary embodiments may include identifying a worker node requiring a least amount of the one or more resources of the one or more worker nodes for training the one or more pipelines.

Abstract: A data mining method, system, and non-transitory computer readable medium, include defining a set of filter constraints as a filter function for clustering users' private records of data of a private domain, selecting a subset of users' public records of data from a filtered set of data from a public domain that is common with the users' private records of data, and creating a data file including the matched user of the private domain to the public records of the user of the private domain, where the set of the filter constraints comprises a function that captures the subset of the users' public records of data who are of interest to the private domain, and only performs data mining with that set of information from the public domain.

Abstract: An encoder includes a computer readable storage medium storing program instructions, and a processor executing the program instructions, the processor configured to generate a key, estimate a network capacity, and encode each bit of the key using a random matrix of a selected rank and the estimated network capacity for secure transmission of the key through a network.

Abstract: A computer-implemented method according to one embodiment includes issuing a hyperparameter optimization (HPO) query to a plurality of computing devices; receiving HPO results from each of the plurality of computing devices; generating a unified performance metric surface utilizing the HPO results from each of the plurality of computing devices; and determining optimal global hyperparameters, utilizing the unified performance metric surface.

Abstract: Using locality sensitive hashing in federated machine learning. A server receives from clients locality sensitive hash (LSH) vectors. In one embodiment, the server groups the clients into clusters, based on the LSH vectors; the server selects a subset of the clients, by choosing at least one client from each of the clusters. In another embodiment, the server finds a subset of the clients, by minimize gradient divergence for the subset of the clients. The server receives from selected clients LSH vectors computed based on parameter vectors of updated models, and based on LSH vectors the server determines whether the updated models are sufficiently different from a model being trained; in response to determining that the updated models are sufficiently different from the model, the server requests the selected clients to send the parameter vectors to the server.

Abstract: Methods are provided. A method includes announcing to a network meta information describing each of a plurality of distributed data sources. The method further includes propagating the meta information amongst routing elements in the network. The method also includes inserting into the network a description of distributed datasets that match a set of requirements of the analytics task. The method additionally includes delivering, by the routing elements, a copy of the analytics task to locations of respective ones of the plurality of distributed data sources that include the distributed datasets that match the set of requirements of the analytics task.

Abstract: A method for targeted advertisement includes transmitting a pre-filter to the user device, responsive to contextual information from a user device, to determine, using a processor, one or more inferences based on physical browsing information, collected at the user device, in compliance with one or more privacy policies of the user. The method also includes receiving one or more inferences determined by the pre-filter from the user device and transmitting one or more targeted advertisements to the user device based on one or more inferences.

Abstract: Methods are provided. A method includes announcing to a network meta information describing each of a plurality of distributed data sources. The method further includes propagating the meta information amongst routing elements in the network. The method also includes inserting into the network a description of distributed datasets that match a set of requirements of the analytics task. The method additionally includes delivering, by the routing elements, a copy of the analytics task to locations of respective ones of the plurality of distributed data sources that include the distributed datasets that match the set of requirements of the analytics task.

Abstract: A model requester node, which is an edge node of a cloud computing network, generates a specification of a machine learning model, distributes the specification to a plurality of other edge nodes, and receives replies to the specification from the plurality of other edge nodes. In response to the replies, the model requester node identifies a set of participating edge nodes based on a learning utility and a cost estimate of each of the plurality of other edge nodes. The model requester node then trains the machine learning model, without exchanging training data among the model requester node and the participating edge nodes, by repeatedly: distributing most recent parameters of the machine learning model to the participating edge nodes; receiving updates to the most recent parameters from the participating edge nodes; and establishing new parameters for the machine learning model by aggregating the updates from the participating edge nodes.