Probes with short service set identifier

    公开(公告)号:US11452080B2

    公开(公告)日:2022-09-20

    申请号:US16717629

    申请日:2019-12-17

    申请人: Intel Corporation

    IPC分类号: H04W72/04 H04W72/12

    摘要: 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.

    Information centric network protocol for federated learning

    公开(公告)号:US12003404B2

    公开(公告)日:2024-06-04

    申请号:US17133314

    申请日:2020-12-23

    申请人: Intel Corporation

    摘要: 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.

    USES OF CODED DATA AT MULTI-ACCESS EDGE COMPUTING SERVER

    公开(公告)号:US20240155025A1

    公开(公告)日:2024-05-09

    申请号:US18550856

    申请日:2022-06-09

    申请人: Intel Corporation

    摘要: 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.

    APPARATUS, SYSTEM, METHOD AND COMPUTER-IMPLEMENTED STORAGE MEDIA TO IMPLEMENT RADIO RESOURCE MANAGEMENT POLICIES USING MACHINE LEARNING

    公开(公告)号:US20220377614A1

    公开(公告)日:2022-11-24

    申请号:US17712050

    申请日:2022-04-01

    申请人: Intel Corporation

    IPC分类号: H04W28/08 H04W28/02

    摘要: 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. The apparatus includes one or more processors to: implement machine learning (ML) based training rounds, each training round including: determining a local action value function Qn(hn, an; θn) corresponding to a value of performing a radio resource management (RRM) action an at a receiving computing node n (RX node n) associated with TX node n using policy parameter θn and based on hn, hn including channel state information at RX node n; and determining, based on an overall action value function Qtot at time t, an estimated gradient of an overall loss at time t for overall policy parameter θt(∇Lt(θt)), wherein Qtot corresponds to a mixing of local action value functions Qi(hi, ai; θi) for all TX nodes i in the network at time t including TX node n; and determine, in response to a determination that ∇Lt(θt) is close to zero for various values of t during training, a trained local action value function Qn,trained to generate a trained action value relating to data communication between TX node n and RX node n.