Abstract: An apparatus of an edge computing node, a method, and a machine-readable storage medium. The apparatus is to decode messages from a plurality of clients within the edge computing network, the messages including respective coded data for respective ones of the plurality of clients; computing estimates of metrics related to a global model for federated learning using the coded data, the metrics including a gradient on the coded data; use the metrics to update the global model to generate an updated global model, wherein the edge computing node is to update the global model by calculating the gradient on the coded data based on a linear fit of the global model to estimated labels from the federated learning; and send a message including the updated global model for transmission to at least some of the clients.

Abstract: A method of implementing distributed AI or ML learning for autonomous vehicles is disclosed. An AI or ML model specific to a location or a type of the location is generated at a vehicle. In response to a detection that the vehicle is within a proximity to a road side unit (RSU) associated with the location or the type of the location or the vehicle is within a proximity to an additional vehicle that is present or anticipated to be present at the location or the type of the location, causing an AI or ML model transmission to the additional vehicle or the RSU. Based on the causing the AI or ML model reception, causing deployment of an additional AI or ML model in the vehicle to optimize the vehicle for the location or the type of the location.

Abstract: Methods, systems, and computer programs are presented for implementing Personalized Mobility as a Service (PMaaS) to improve transportation services delivery. One storage medium includes instructions for detecting, by a mobility as a service (MaaS) system, a request for a trip from a user device of a user. The storage medium further includes instructions for mapping, using a model executing on the machine, the user to a persona from a plurality of persona models. Each persona model has one or more characteristics associated with users of the MaaS system. Further yet, the storage medium includes instructions for determining trip parameters for the trip based on the persona mapped to the user, the trip parameters defining one or more trip segments for the trip, and instructions for providing trip parameters to the user device.

Abstract: Methods, apparatus, systems, and articles of manufacture are disclosed that coordinate network traffic between a wireless network device and a computing platform. An example apparatus includes a wake-up selector to generate a target wait time parameter based on a workload type of a number of packets obtained from a network device and a user preference, the target wait time parameter indicative of a time interval that, when met, causes a modem to retrieve the number of packets, a data frame generator to generate a data frame that causes the network device to buffer the number of packets for the time interval, and a network packet controller to negotiate, using the data frame, the target wait time parameter with a network device.

Abstract: A method can be performed by a first node for determining a parameter of physical (PHY) layer circuitry of a second node. The method can include implementing a cascaded hierarchy of techniques to determine, based on an electrical signal from a second node, a parameter of the PHY layer circuitry of the second node, and causing an antenna of the first node to transmit an electromagnetic wave consistent with the determined parameter.

Abstract: Methods, apparatus, systems, and articles of manufacture are disclosed that generate dynamic latency values. An example apparatus includes an active status controller to determine that a modem is active based on a number of packets obtained from a network, a prediction controller to predict that the number of packets are indicative of a workload type based on a trained model, and a latency value generator to generate a latency value based on the workload type of the number of packets, the latency value to cause a processor processing the number of packets to enter a power saving state or a power executing state.

Abstract: System and techniques for information centric network (ICN) protocol for federated learning are described herein. An interest packet may be received on a first interface to start a federated learning round. Here, the interest packet includes a participant criterion and a federated learning round expiration. An entry, that includes the federated learning round expiration, is created in a pending interest table (PIT) for the interest packet. The interest packet is forwarded, in accordance with a forwarding information base (FIB), to a set of interfaces before the federated learning round expiration. When a data packet from a node, that meeting the participant criterion, is received in response to the interest packet, the data packet is forwarded on the first interface in accordance with the PIT entry.

Abstract: An apparatus of an edge computing node, a method, and a machine-readable storage medium. The apparatus is to decode messages from a plurality of clients within the edge computing network, the messages including respective coded data for respective ones of the plurality of clients; computing estimates of metrics related to a global model for federated learning using the coded data, the metrics including a gradient on the coded data; use the metrics to update the global model to generate an updated global model, wherein the edge computing node is to update the global model by calculating the gradient on the coded data based on a linear fit of the global model to estimated labels from the federated learning; and send a message including the updated global model for transmission to at least some of the clients.

Abstract: Systems and techniques for information centric network (ICN) interworking are described herein. For example, a request may be received at a convergence layer of a node. Here, the request originates from an application on the node. A network protocol, from several available to the node, may be determined to transmit the request. The node then transmits the request via the selected network protocol.

Abstract: Techniques for non-linear distributed multitask support vector machines are disclosed. In the illustrative embodiment, a coordinator node sends initial parameters (or a random number generator along with model choice) for a global model to participant nodes. Each participant node performs a round of training based on the common global model parameters, the model models, and local data. Each participant node determines updated parameters for the global model and updated parameters for a local model. Each participant node sends an update of the parameters to the global model to the coordinator node, while keeping the parameters of the local model private. The coordinator node aggregates the updates from the participant nodes, updates the global model parameters, and sends them back to the participant nodes. The process can repeat until a desired error level is reached.

Abstract: A task modeling system, including a plurality of processing clients having a plurality of processing cores; a task modeler, including a memory storing an artificial neural network; and a processor, configured to receive input data representing a plurality of processing tasks to be completed by the processing client within a predefined time duration; and implement its artificial neural network to determine from the input data an assignment of the processing tasks among the processing cores for completion of the processing tasks within the predefined time duration, and determine a power management factor for each of the plurality of processing cores for power management during the predefined time duration; wherein the artificial neural network is configured to select the power management factor for each of the plurality of processing cores to achieve a power usage within a predefined threshold for the plurality of processing cores during the predefined time duration.

Abstract: In one embodiment, a machine learning (ML) model for determining radio resource management (RRM) decisions is updated, with ML model parameters being shared between RRM decision makers to update the model. The updates may include local operations (between an AP and UE pair) to update local primal and dual parameters of the ML model, and global operations (between other devices in the network) to exchange/update global parameters of the ML model.

Abstract: The apparatus of an edge computing node, a system, a method and a machine-readable medium. The apparatus includes a processor to cause an initial set of weights for a global machine learning (ML) model to be transmitted a set of client compute nodes of the edge computing network; process Hessians computed by each of the client compute nodes based on a dataset stored on the client compute node; evaluate a gradient expression for the ML model based on a second dataset and an updated set of weights received from the client compute nodes; and generate a meta-updated set of weights for the global model based on the initial set of weights, the Hessians received, and the evaluated gradient expression.

Abstract: Systems and methods of using machine-learning to improve communications across different networks are described. A CIRN node identifies whether it is within range of a source and destination node in a different network using explicit information or a machine-learning classification model. A neural network is trained to avoid interference using rewards associated with reduced interference or retransmission levels in each network or improved throughput at the CIRN node. A machine-learning scheduling algorithm determines a relay mode of the CIRN node for source and destination node transmissions. The scheduling algorithm is based on the probability of successful transmission between the source and destination nodes multiplied by a collaboration score for successful transmission and the probability of unsuccessful transmission of the particular packet multiplied by a collaboration score for unsuccessful transmission.

Abstract: The apparatus of an edge computing node, a system, a method and a machine-readable medium. The apparatus includes a processor to perform rounds of federated machine learning training including: processing client reports from a plurality of clients of the edge computing network; selecting a candidate set of clients from the plurality of clients for an epoch of the federated machine learning training; causing a global model to be sent to the candidate set of clients; and performing the federated machine learning training on the candidate set of clients. The processor may perform rounds of federated machine learning training including: obtaining coded training data from each of the selected clients; and performing machine learning training on the coded training data.

Abstract: Systems and techniques for information-centric network namespace policy-based content delivery are described herein. A registration request may be received from a node on an information-centric network (ICN). Credentials of the node may be validated. The node may be registered with the ICN based on results of the validation. A set of content items associated with the node may be registered with the ICN. An interest packet may be received from a consumer node for a content item of the set of content items that includes an interest packet security level for the content item. Compliance of the security level of the node with the interest packet security level may be determined. The content item may be transmitted to the consumer node.

Abstract: An apparatus of a transmitter computing node n (TX node n) of a wireless network, one or more computer readable media, a system, and a method.

Abstract: This disclosure describes systems, methods, and devices related to probes with service set identifier (SSID). A device may determine one or more access points (APs) in an enterprise extended service set (ESS). The device may identify a probe request received from a first station device, wherein the probe request comprises a service set element, wherein the service set element comprises a variable number of service set fields based on a number of APs in the ESS. The device may determine that a service set that matches at least one of the service set fields in the service set element. The device may cause to send a probe response to the first station device in response to the probe request.

Abstract: This disclosure describes systems, methods, and devices related to extremely high throughput (EHT) multiuser hybrid automatic repeat request (HARQ). A device may determine one or more medium access control (MAC) protocol data unit (MPDUs) to be sent to a first station device of one or more station devices, wherein the one or more MAC protocol data units (MPDUs) comprise a first MPDU. The device may segment the first MPDU into a plurality of segments, wherein the one or more segments include a first segment and a second segment. The device may cause to send the plurality of segments to the first station device. The device may identify a feedback frame received from the first station device, wherein the feedback frame comprises error information associated with the plurality of segments. The device may cause to retransmit at least one of the plurality of segments based on the error information.

Abstract: To address technical problems facing managing multiple sources of information from multiple vehicles, vehicular computing power may be exploited to process such information before sharing with others, which may help reduce network traffic overhead. A technical solution to improve this information processing over vehicular networks by using a hybrid Named Function Network (NFN) and Information Centric Network (ICN), such as in a hybrid NFN/ICN. An NFN may be used to orchestrate computations in a highly dynamic environment after decomposing the computations into a number of small functions. A function may include a digitally signed binary supplied by a car vendor or other trusted authority and executed within a controlled environment, such as a virtual machine, container, Java runtime-environment, or other controlled environment.