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    FEDERATED LEARNING WITH VARYING FEEDBACK
    143.
    发明申请
    FEDERATED LEARNING WITH VARYING FEEDBACK 有权

    公开(公告)号：US20220101131A1

    公开(公告)日：2022-03-31

    申请号：US17479916

    申请日：2021-09-20

    Applicant: QUALCOMM Incorporated

    Inventor： Mahmoud TAHERZADEH BOROUJENI ,  Tao LUO ,  Hamed PEZESHKI ,  Vinay CHANDE ,  Taesang YOO

    IPC: G06N3/08 ,  G06N3/04 ,  G06F17/18

    Abstract: A method of wireless communication, by a user equipment (UE) includes receiving, from a base station, a jointly trained artificial neural network. The method also includes calculating a value representing at least one of (1) a gradient estimate for a weight of the jointly trained artificial neural network, or (2) the weight of the jointly trained artificial neural network. The method further includes expanding the value into a numerical system with base N into a plurality of digits. The method also includes determining a number and/or a location of the plurality of digits to transmit based on a deterministic task assignment rule received from the base station or a probabilistic task assignment rule. The method further includes transmitting the determined number and/or the determined location of the plurality of digits to the base station.

    QUANTIZED FEEDBACK IN FEDERATED LEARNING WITH RANDOMIZATION
    144.
    发明申请
    QUANTIZED FEEDBACK IN FEDERATED LEARNING WITH RANDOMIZATION 有权

    公开(公告)号：US20220101130A1

    公开(公告)日：2022-03-31

    申请号：US17448298

    申请日：2021-09-21

    Applicant: QUALCOMM Incorporated

    Inventor： Mahmoud TAHERZADEH BOROUJENI ,  Taesang YOO ,  Tao LUO ,  Hamed PEZESHKI

    IPC: G06N3/08 ,  H04L29/06

    Abstract: Various aspects of the present disclosure generally relate to wireless communication. In some aspects, a client device may determine a feedback associated with a machine learning component based at least in part on applying the machine learning component. Accordingly, the client device may transmit a quantized value based at least in part on the feedback. The quantized value is determined using randomization with probabilities based at least in part on respective distances between one or more values of the feedback and a plurality of quantized digits. Numerous other aspects are provided.

    Neural Augmentation For Device Nonlinearity Mitigation In X-Node Machine Learning
    146.
    发明申请
    Neural Augmentation For Device Nonlinearity Mitigation In X-Node Machine Learning 有权

    公开(公告)号：US20220070041A1

    公开(公告)日：2022-03-03

    申请号：US17394928

    申请日：2021-08-05

    Applicant: QUALCOMM Incorporated

    Inventor： June NAMGOONG ,  Taesang YOO ,  Naga BHUSHAN ,  Krishna Kiran MUKKAVILLI ,  Tingfang JI ,  Hwan Joon KWON ,  Pavan Kumar VITTHALADEVUNI ,  Jay Kumar SUNDARARAJAN

    IPC: H04L27/26 ,  G06N3/04

    Abstract: Various embodiments include methods for reducing peak to average power ratio of wireless transmission waveforms, performed in transmitter circuitry of a wireless communication. Various embodiments may include receiving frequency domain data tones, transforming the frequency domain data tones to time domain data signals, generating, using a set of peak reduction tone (PRT) neural networks, time domain PRTs using the time domain data signals in which the set of PRT neural networks have been trained in conjunction with an augmentation neural network, and a receiver neural network, generating an output of the augmentation neural network based on an input of final combined time domain signals including the time domain PRTs combined with previous combined time domain signals, and generating time domain wireless transmission waveforms that include the output of the augmentation neural network combined with the final combined time domain signals.

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