Patent Description:
Wireless communications systems are widely deployed to provide various telecommunications services such as telephony, video, data, messaging, and broadcasts. Typical wireless communications systems may employ multiple-access technologies capable of supporting communications with multiple users by sharing available system resources (e.g., bandwidth, transmit power, and/or the like). Examples of such multiple-access technologies include code division multiple access (CDMA) systems, time division multiple access (TDMA) systems, frequency-division multiple access (FDMA) systems, orthogonal frequency-division multiple access (OFDMA) systems, single-carrier frequency-division multiple access (SC-FDMA) systems, time division synchronous code division multiple access (TD-SCDMA) systems, and long term evolution (LTE). LTE/LTE-Advanced is a set of enhancements to the universal mobile telecommunications system (UMTS) mobile standard promulgated by the Third Generation Partnership Project (3GPP).

A wireless communications network may include a number of base stations (BSs) that can support communications for a number of user equipment (UEs). The downlink (or forward link) refers 'to the communications link from the BS to the UE, and the uplink (or reverse link) refers to the communications link from the UE to the BS. As will be described in more detail, a BS may be referred to as a Node B, a gNB, an access point (AP), a radio head, a transmit and receive point (TRP), a new radio (NR) BS, a <NUM> Node B, and/or the like.

The above multiple access technologies have been adopted in various telecommunications standards to provide a common protocol that enables different user equipment to communicate on a municipal, national, regional, and even global level.

Artificial neural networks may comprise interconnected groups of artificial neurons (e.g., neuron models). The artificial neural network may be a computational device or represented as a method to be performed by a computational device. Convolutional neural networks, such as deep convolutional neural networks, are a type of feed-forward artificial neural network. Convolutional neural networks may include layers of neurons that may be configured in a tiled receptive field. It would be desirable to apply neural network processing to wireless communications to achieve greater efficiencies.

<NPL>; "<NPL>, <NPL> discloses general principles of federated learning over 5GS.

The invention is described herein with reference to the appended claims.

So that features of the present disclosure can be understood in detail, a particular description may be had by reference to aspects, some of which are illustrated in the appended drawings.

Various aspects of the disclosure are described more fully below with reference to the accompanying drawings.

Several aspects of telecommunications systems will now be presented with reference to various apparatuses and techniques.

It should be noted that while aspects may be described using terminology commonly associated with <NUM> and later wireless technologies, aspects of the present disclosure can be applied in other generation-based communications systems, such as and including <NUM> and/or <NUM> technologies.

Standard machine learning approaches centralize training data on one machine, or in a data center. A federated learning model supports collaborative learning of a shared prediction model among user equipment (UEs) and a base station (or centralized server). Federated learning is a process where a group of UEs receives a machine learning model from a base station and work together to train the model. More specifically, each UE trains the model locally, and sends back either updated neural network model weights or gradient updates from, for example, a locally performed stochastic gradient descent process. The base station receives the updates from all of the UEs in the group and aggregates them, for example by averaging them, to obtain updated global weights of the neural network. The base station sends the updated model to the UEs, and the process repeats, round after round, until a desired performance level from the global model is obtained.

In each round of a federated learning process, a group of UEs sends back weights or gradient updates within a given time interval after they receive the model from the base station. If a UE misses the deadline for sending updates, the weights or gradients will become stale, and the base station will not incorporate the update in the weight or gradient aggregation for that local training round of the federated learning process.

According to aspects of the present disclosure, a UE reports its machine learning processing capability to the base station. In some aspects, the report indicate a machine learning hardware capability. In other aspects, the report indicates an approximate turnaround time for computing the gradient or weight updates in each of the federated learning rounds. In still other aspects of the present disclosure, the UE reports an approximate turnaround time for computing the gradients or weights, for example, as a function of battery status of the UE.

The reported machine learning hardware capability provides the base station with an approximate training time at the UE side, for preparing each gradient or weight update. For example, the base station can decide whether a reporting UE is a fast UE or a slow UE based on the reported machine learning capability. Consequently, the base station groups the UEs for different federated learning rounds according to machine learning capability. Slow UEs may be grouped with other slower UEs, while fast UEs are grouped with other faster UEs, improving efficiency of the federated learning process.

The network <NUM> may be a <NUM> or NR network or some other wireless network, such as an LTE network. A BS is an entity that communicates with user equipment (UEs) and may also be referred to as a base station, a NR BS, a Node B, a gNB, a <NUM> node B (NB), an access point, a transmit and receive point (TRP), and/or the like. Each BS may provide communications coverage for a particular geographic area.

A BS may provide communications coverage for a macro cell, a pico cell, a femto cell, and/or another type of cell. The terms "eNB," "base station," "NR BS," "gNB," "TRP," "AP," "node B," "<NUM> NB," and "cell" may be used interchangeably.

In the example shown in <FIG>, a relay station 110d may communicate with macro BS 110a and a UE 120d in order to facilitate communications between the BS 110a and UE 120d.

The wireless network <NUM> may be a heterogeneous network that includes BSs of different types, e.g., macro BSs, pico BSs, femto BSs, relay BSs, and/or the like. These different types of BSs may have different transmit power levels, different coverage areas, and different impact on interference in the wireless network <NUM>.

The network controller <NUM> may communicate with the BSs via a backhaul.

UEs <NUM> (e.g., 120a, 120b, 120c) may be dispersed throughout the wireless network <NUM>, and each UE may be stationary or mobile. A UE may be a cellular phone (e.g., a smart phone), a personal digital assistant (PDA), a wireless modem, a wireless communications device, a handheld device, a laptop computer, a cordless phone, a wireless local loop (WLL) station, a tablet, a camera, a gaming device, a netbook, a smartbook, an ultrabook, a medical device or equipment, biometric sensors/devices, wearable devices (smart watches, smart clothing, smart glasses, smart wrist bands, smart jewelry (e.g., smart ring, smart bracelet)), an entertainment device (e.g., a music or video device, or a satellite radio), a vehicular component or sensor, smart meters/sensors, industrial manufacturing equipment, a global positioning system device, or any other suitable device that is configured to communicate via a wireless or wired medium.

Some UEs may be considered machine-type communications (MTC) or evolved or enhanced machine-type communications (eMTC) UEs. A wireless node may provide, for example, connectivity for or to a network (e.g., a wide area network such as Internet or a cellular network) via a wired or wireless communications link. Some UEs may be considered a customer premises equipment (CPE).

In this case, the UE <NUM> may perform scheduling operations, resource selection operations, and/or other operations described elsewhere as being performed by the base station <NUM>. For example, the base station <NUM> may configure a UE <NUM> via downlink control information (DCI), radio resource control (RRC) signaling, a media access control-control element (MAC-CE) or via system information (e.g., a system information block (SIB).

The UEs <NUM> may include a machine learning (ML) capability reporting module <NUM>. For brevity, only one UE 120d is shown as including the ML capability reporting module <NUM>. The ML capability reporting module <NUM> may receive a machine learning model from a base station, and report a machine learning processing capability to the base station. The ML capability reporting module <NUM> may also transmit, to the base station, gradient updates or weight updates to the machine learning model.

The base stations <NUM> may include an ML capability grouping module <NUM>. For brevity, only one base station 110a is shown as including the ML capability reporting module <NUM>. The ML capability group module <NUM> may transmit a machine learning model to multiple user equipment (UEs). The ML capability group module <NUM> may also receive, from each of the UEs, a machine learning processing capability report. The ML capability group module <NUM> may further group the UEs in accordance with the machine learning processing capability reports, for receiving gradient updates to the machine learning model.

<FIG> shows a block diagram of a design <NUM> of the base station <NUM> and UE <NUM>, which may be one of the base stations and one of the UEs in <FIG>. The base station <NUM> may be equipped with T antennas 234a through 234t, and UE <NUM> may be equipped with R antennas 252a through 252r, where in general T ≥ <NUM> and R ≥ <NUM>.

At the base station <NUM>, a transmit processor <NUM> may receive data from a data source <NUM> for one or more UEs, select one or more modulation and coding schemes (MCS) for each UE based at least in part on channel quality indicators (CQIs) received from the UE, process (e.g., encode and modulate) the data for each UE based at least in part on the MCS(s) selected for the UE, and provide data symbols for all UEs. Decreasing the MCS lowers throughput but increases reliability of the transmission. The transmit processor <NUM> may also process system information (e.g., for semi-static resource partitioning information (SRPI) and/or the like) and control information (e.g., CQI requests, grants, upper layer signaling, and/or the like) and provide overhead symbols and control symbols. The transmit processor <NUM> may also generate reference symbols for reference signals (e.g., the cell-specific reference signal (CRS)) and synchronization signals (e.g., the primary synchronization signal (PSS) and secondary synchronization signal (SSS)).

At the UE <NUM>, antennas 252a through 252r may receive the downlink signals from the base station <NUM> and/or other base stations and may provide received signals to demodulators (DEMODs) 254a through 254r, respectively. A receive processor <NUM> may process (e.g., demodulate and decode) the detected symbols, provide decoded data for the UE <NUM> to a data sink <NUM>, and provide decoded control information and system information to a controller/processor <NUM>. In some aspects, one or more components of the UE <NUM> may be included in a housing.

On the uplink, at the UE <NUM>, a transmit processor <NUM> may receive and process data from a data source <NUM> and control information (e.g., for reports comprising RSRP, RSSI, RSRQ, CQI, and/or the like) from the controller/processor <NUM>. The symbols from the transmit processor <NUM> may be precoded by a TX MIMO processor <NUM> if applicable, further processed by modulators 254a through 254r (e.g., for DFT-s-OFDM, CP-OFDM, and/or the like), and transmitted to the base station <NUM>. At the base station <NUM>, the uplink signals from the UE <NUM> and other UEs may be received by the antennas <NUM>, processed by the demodulators <NUM>, detected by a MIMO detector <NUM> if applicable, and further processed by a receive processor <NUM> to obtain decoded data and control information sent by the UE <NUM>. The receive processor <NUM> may provide the decoded data to a data sink <NUM> and the decoded control information to a controller/processor <NUM>. The base station <NUM> may include communications unit <NUM> and communicate to the network controller <NUM> via the communications unit <NUM>. The network controller <NUM> may include a communications unit <NUM>, a controller/processor <NUM>, and a memory <NUM>.

The controller/processor <NUM> of the base station <NUM>, the controller/processor <NUM> of the UE <NUM>, and/or any other component(s) of <FIG> may perform one or more techniques associated with machine learning capability reporting, as described in more detail elsewhere. For example, the controller/processor <NUM> of the base station <NUM>, the controller/processor <NUM> of the UE <NUM>, and/or any other component(s) of <FIG> may perform or direct operations of, for example, the processes of <FIG> and/or other processes as described. Memories <NUM> and <NUM> may store data and program codes for the base station <NUM> and UE <NUM>, respectively.

In some aspects, the UE <NUM> or base station <NUM> may include means for receiving, means for reporting, means for transmitting, means for grouping, and/or means for scheduling. Such means may include one or more components of the UE <NUM> or base station <NUM> described in connection with <FIG>.

In some cases, different types of devices supporting different types of applications and/or services may coexist in a cell. Examples of different types of devices include UE handsets, customer premises equipment (CPEs), vehicles, Internet of Things (IoT) devices, and/or the like. Examples of different types of applications include ultra-reliable low-latency communications (URLLC) applications, massive machine-type communications (mMTC) applications, enhanced mobile broadband (eMBB) applications, vehicle-to-anything (V2X) applications, and/or the like. Furthermore, in some cases, a single device may support different applications or services simultaneously.

<FIG> illustrates an example implementation of a system-on-a-chip (SOC) <NUM>, which may include a central processing unit (CPU) <NUM> or a multi-core CPU configured for generating gradients for neural network training, in accordance with certain aspects of the present disclosure. The SOC <NUM> may be included in the base station <NUM> or UE <NUM>. Variables (e.g., neural signals and synaptic weights), system parameters associated with a computational device (e.g., neural network with weights), delays, frequency bin information, and task information may be stored in a memory block associated with a neural processing unit (NPU) <NUM>, in a memory block associated with a CPU <NUM>, in a memory block associated with a graphics processing unit (GPU) <NUM>, in a memory block associated with a digital signal processor (DSP) <NUM>, in a memory block <NUM>, or may be distributed across multiple blocks. Instructions executed at the CPU <NUM> may be loaded from a program memory associated with the CPU <NUM> or may be loaded from a memory block <NUM>.

The SOC <NUM> may also include additional processing blocks tailored to specific functions, such as a GPU <NUM>, a DSP <NUM>, a connectivity block <NUM>, which may include fifth generation (<NUM>) connectivity, fourth generation long term evolution (<NUM> LTE) connectivity, Wi-Fi connectivity, USB connectivity, Bluetooth connectivity, and the like, and a multimedia processor <NUM> that may, for example, detect and recognize gestures. In one implementation, the NPU is implemented in the CPU, DSP, and/or GPU. The SOC <NUM> may also include a sensor processor <NUM>, image signal processors (ISPs) <NUM>, and/or navigation module <NUM>, which may include a global positioning system.

The SOC <NUM> may be based on an ARM instruction set. In aspects of the present disclosure, the instructions loaded into the general-purpose processor <NUM> may comprise code to receive a machine learning model from a base station. The general-purpose processor <NUM> may also comprise code to report, to the base station, a machine learning processing capability. The general-purpose processor <NUM> may further comprise code to transmit, to the base station, gradient updates or weight updates to the machine learning model. In other aspects of the present disclosure, the instructions loaded into the general-purpose processor <NUM> may comprise code to transmit a machine learning model to multiple user equipment (UEs); and code to receive, from each of the of UEs, a machine learning processing capability report. The instructions loaded into the general-purpose processor <NUM> may also comprise code to group the UEs in accordance with the machine learning processing capability reports, for receiving gradient updates to the machine learning model.

Deep learning architectures may perform an object recognition task by learning to represent inputs at successively higher levels of abstraction in each layer, thereby building up a useful feature representation of the input data. In this way, deep learning addresses a major bottleneck of traditional machine learning. Prior to the advent of deep learning, a machine learning approach to an object recognition problem may have relied heavily on human engineered features, perhaps in combination with a shallow classifier. A shallow classifier may be a two-class linear classifier, for example, in which a weighted sum of the feature vector components may be compared with a threshold to predict to which class the input belongs. Human engineered features may be templates or kernels tailored to a specific problem domain by engineers with domain expertise. Deep learning architectures, in contrast, may learn to represent features that are similar to what a human engineer might design, but through training. Furthermore, a deep network may learn to represent and recognize new types of features that a human might not have considered.

A deep learning architecture may learn a hierarchy of features. If presented with visual data, for example, the first layer may learn to recognize relatively simple features, such as edges, in the input stream. In another example, if presented with auditory data, the first layer may learn to recognize spectral power in specific frequencies. The second layer, taking the output of the first layer as input, may learn to recognize combinations of features, such as simple shapes for visual data or combinations of sounds for auditory data. For instance, higher layers may learn to represent complex shapes in visual data or words in auditory data. Still higher layers may learn to recognize common visual objects or spoken phrases.

Deep learning architectures may perform especially well when applied to problems that have a natural hierarchical structure. For example, the classification of motorized vehicles may benefit from first learning to recognize wheels, windshields, and other features. These features may be combined at higher layers in different ways to recognize cars, trucks, and airplanes.

Neural networks may be designed with a variety of connectivity patterns. In feed-forward networks, information is passed from lower to higher layers, with each neuron in a given layer communicating to neurons in higher layers. A hierarchical representation may be built up in successive layers of a feed-forward network, as described above. Neural networks may also have recurrent or feedback (also called top-down) connections. In a recurrent connection, the output from a neuron in a given layer may be communicated to another neuron in the same layer. A recurrent architecture may be helpful in recognizing patterns that span more than one of the input data chunks that are delivered to the neural network in a sequence. A connection from a neuron in a given layer to a neuron in a lower layer is called a feedback (or top-down) connection. A network with many feedback connections may be helpful when the recognition of a high-level concept may aid in discriminating the particular low-level features of an input.

The connections between layers of a neural network may be fully connected or locally connected. <FIG> illustrates an example of a fully connected neural network <NUM>. In a fully connected neural network <NUM>, a neuron in a first layer may communicate its output to every neuron in a second layer, so that each neuron in the second layer will receive input from every neuron in the first layer. <FIG> illustrates an example of a locally connected neural network <NUM>. In a locally connected neural network <NUM>, a neuron in a first layer may be connected to a limited number of neurons in the second layer. More generally, a locally connected layer of the locally connected neural network <NUM> may be configured so that each neuron in a layer will have the same or a similar connectivity pattern, but with connections strengths that may have different values (e.g., <NUM>, <NUM>, <NUM>, and <NUM>). The locally connected connectivity pattern may give rise to spatially distinct receptive fields in a higher layer, because the higher layer neurons in a given region may receive inputs that are tuned through training to the properties of a restricted portion of the total input to the network.

One example of a locally connected neural network is a convolutional neural network. <FIG> illustrates an example of a convolutional neural network <NUM>. The convolutional neural network <NUM> may be configured such that the connection strengths associated with the inputs for each neuron in the second layer are shared (e.g., <NUM>). Convolutional neural networks may be well suited to problems in which the spatial location of inputs is meaningful.

One type of convolutional neural network is a deep convolutional network (DCN). <FIG> illustrates a detailed example of a DCN <NUM> designed to recognize visual features from an image <NUM> input from an image capturing device <NUM>, such as a car-mounted camera. The DCN <NUM> of the current example may be trained to identify traffic signs and a number provided on the traffic sign. Of course, the DCN <NUM> may be trained for other tasks, such as identifying lane markings or identifying traffic lights.

The DCN <NUM> may be trained with supervised learning. During training, the DCN <NUM> may be presented with an image, such as the image <NUM> of a speed limit sign, and a forward pass may then be computed to produce an output <NUM>. The DCN <NUM> may include a feature extraction section and a classification section. Upon receiving the image <NUM>, a convolutional layer <NUM> may apply convolutional kernels (not shown) to the image <NUM> to generate a first set of feature maps <NUM>. As an example, the convolutional kernel for the convolutional layer <NUM> may be a 5x5 kernel that generates 28x28 feature maps. In the present example, because four different feature maps are generated in the first set of feature maps <NUM>, four different convolutional kernels were applied to the image <NUM> at the convolutional layer <NUM>. The convolutional kernels may also be referred to as filters or convolutional filters.

The first set of feature maps <NUM> may be subsampled by a max pooling layer (not shown) to generate a second set of feature maps <NUM>. The max pooling layer reduces the size of the first set of feature maps <NUM>. That is, a size of the second set of feature maps <NUM>, such as 14x14, is less than the size of the first set of feature maps <NUM>, such as 28x28. The reduced size provides similar information to a subsequent layer while reducing memory consumption. The second set of feature maps <NUM> may be further convolved via one or more subsequent convolutional layers (not shown) to generate one or more subsequent sets of feature maps (not shown).

In the example of <FIG>, the second set of feature maps <NUM> is convolved to generate a first feature vector <NUM>. Furthermore, the first feature vector <NUM> is further convolved to generate a second feature vector <NUM>. Each feature of the second feature vector <NUM> may include a number that corresponds to a possible feature of the image <NUM>, such as "sign," "<NUM>," and "<NUM>. " A softmax function (not shown) may convert the numbers in the second feature vector <NUM> to a probability. As such, an output <NUM> of the DCN <NUM> is a probability of the image <NUM> including one or more features.

In the present example, the probabilities in the output <NUM> for "sign" and "<NUM>" are higher than the probabilities of the others of the output <NUM>, such as "<NUM>," "<NUM>," "<NUM>," "<NUM>," "<NUM>," "<NUM>," and "<NUM>". Before training, the output <NUM> produced by the DCN <NUM> is likely to be incorrect. Thus, an error may be calculated between the output <NUM> and a target output. The target output is the ground truth of the image <NUM> (e.g., "sign" and "<NUM>"). The weights of the DCN <NUM> may then be adjusted so the output <NUM> of the DCN <NUM> is more closely aligned with the target output.

To adjust the weights, a learning algorithm may compute a gradient vector for the weights. The gradient may indicate an amount that an error would increase or decrease if the weight were adjusted. At the top layer, the gradient may correspond directly to the value of a weight connecting an activated neuron in the penultimate layer and a neuron in the output layer. In lower layers, the gradient may depend on the value of the weights and on the computed error gradients of the higher layers. The weights may then be adjusted to reduce the error. This manner of adjusting the weights may be referred to as "back propagation" as it involves a "backward pass" through the neural network.

In practice, the error gradient of weights may be calculated over a small number of examples, so that the calculated gradient approximates the true error gradient. This approximation method may be referred to as stochastic gradient descent. Stochastic gradient descent may be repeated until the achievable error rate of the entire system has stopped decreasing or until the error rate has reached a target level. After learning, the DCN may be presented with new images (e.g., the speed limit sign of the image <NUM>) and a forward pass through the network may yield an output <NUM> that may be considered an inference or a prediction of the DCN.

Deep belief networks (DBNs) are probabilistic models comprising multiple layers of hidden nodes. DBNs may be used to extract a hierarchical representation of training data sets. A DBN may be obtained by stacking up layers of Restricted Boltzmann Machines (RBMs). An RBM is a type of artificial neural network that can learn a probability distribution over a set of inputs. Because RBMs can learn a probability distribution in the absence of information about the class to which each input should be categorized, RBMs are often used in unsupervised learning. Using a hybrid unsupervised and supervised paradigm, the bottom RBMs of a DBN may be trained in an unsupervised manner and may serve as feature extractors, and the top RBM may be trained in a supervised manner (on a joint distribution of inputs from the previous layer and target classes) and may serve as a classifier.

Deep convolutional networks (DCNs) are networks of convolutional networks, configured with additional pooling and normalization layers. DCNs have achieved state-of-the-art performance on many tasks. DCNs can be trained using supervised learning in which both the input and output targets are known for many exemplars and are used to modify the weights of the network by use of gradient descent methods.

DCNs may be feed-forward networks. In addition, as described above, the connections from a neuron in a first layer of a DCN to a group of neurons in the next higher layer are shared across the neurons in the first layer. The feed-forward and shared connections of DCNs may be exploited for fast processing. The computational burden of a DCN may be much less, for example, than that of a similarly sized neural network that comprises recurrent or feedback connections.

The processing of each layer of a convolutional network may be considered a spatially invariant template or basis projection. If the input is first decomposed into multiple channels, such as the red, green, and blue channels of a color image, then the convolutional network trained on that input may be considered three-dimensional, with two spatial dimensions along the axes of the image and a third dimension capturing color information. The outputs of the convolutional connections may be considered to form a feature map in the subsequent layer, with each element of the feature map (e.g., <NUM>) receiving input from a range of neurons in the previous layer (e.g., feature maps <NUM>) and from each of the multiple channels. The values in the feature map may be further processed with a non-linearity, such as a rectification, max(<NUM>, x). Values from adjacent neurons may be further pooled, which corresponds to down sampling, and may provide additional local invariance and dimensionality reduction. Normalization, which corresponds to whitening, may also be applied through lateral inhibition between neurons in the feature map.

The performance of deep learning architectures may increase as more labeled data points become available or as computational power increases. Modern deep neural networks are routinely trained with computing resources that are thousands of times greater than what was available to a typical researcher just fifteen years ago. New architectures and training paradigms may further boost the performance of deep learning. Rectified linear units may reduce a training issue known as vanishing gradients. New training techniques may reduce over-fitting and thus enable larger models to achieve better generalization. Encapsulation techniques may abstract data in a given receptive field and further boost overall performance.

<FIG> is a block diagram illustrating a deep convolutional network <NUM>. The deep convolutional network <NUM> may include multiple different types of layers based on connectivity and weight sharing. As shown in <FIG>, the deep convolutional network <NUM> includes the convolution blocks 554A, 554B. Each of the convolution blocks 554A, 554B may be configured with a convolution layer (CONV) <NUM>, a normalization layer (LNorm) <NUM>, and a max pooling layer (MAX POOL) <NUM>.

The convolution layers <NUM> may include one or more convolutional filters, which may be applied to the input data to generate a feature map. Although only two of the convolution blocks 554A, 554B are shown, the present disclosure is not so limiting, and instead, any number of the convolution blocks 554A, 554B may be included in the deep convolutional network <NUM> according to design preference. The normalization layer <NUM> may normalize the output of the convolution filters. For example, the normalization layer <NUM> may provide whitening or lateral inhibition. The max pooling layer <NUM> may provide down sampling aggregation over space for local invariance and dimensionality reduction.

The parallel filter banks, for example, of a deep convolutional network may be loaded on a CPU <NUM> or GPU <NUM> of an SOC <NUM> to achieve high performance and low power consumption. In alternative embodiments, the parallel filter banks may be loaded on the DSP <NUM> or an ISP <NUM> of an SOC <NUM>. In addition, the deep convolutional network <NUM> may access other processing blocks that may be present on the SOC <NUM>, such as sensor processor <NUM> and navigation module <NUM>, dedicated, respectively, to sensors and navigation.

The deep convolutional network <NUM> may also include one or more fully connected layers <NUM> (FC1 and FC2). The deep convolutional network <NUM> may further include a logistic regression (LR) layer <NUM>. Between each layer <NUM>, <NUM>, <NUM>, <NUM>, <NUM> of the deep convolutional network <NUM> are weights (not shown) that are to be updated. The output of each of the layers (e.g., <NUM>, <NUM>, <NUM>, <NUM>, <NUM>) may serve as an input of a succeeding one of the layers (e.g., <NUM>, <NUM>, <NUM>, <NUM>, <NUM>) in the deep convolutional network <NUM> to learn hierarchical feature representations from input data <NUM> (e.g., images, audio, video, sensor data and/or other input data) supplied at the first of the convolution blocks 554A. The output of the deep convolutional network <NUM> is a classification score <NUM> for the input data <NUM>. The classification score <NUM> may be a set of probabilities, where each probability is the probability of the input data, including a feature from a set of features.

As noted above, standard machine learning approaches centralize training data on one machine, or in a data center. In contrast, federated learning is a process where a group of UEs receives a machine learning model from a base station and work together to train the model. More specifically, each UE trains the model locally, and sends back either updated neural network model weights or gradient updates from, for example, a locally performed stochastic gradient descent process. The base station receives the updates from all of the UEs in the group and aggregates them, for example by averaging them, to obtain updated global weights of the neural network. The base station sends the updated model to the UEs, and the process repeats, round after round, until a desired performance level from the global model is obtained.

In each round of a federated learning process, a group of UEs sends back weights or gradient updates within a given time interval after they receive the model from the base station. If a UE misses a deadline for sending updates, the weights or gradients will become stale, and the base station will not incorporate the update in the weight or gradient aggregation for that round of the federated learning process.

According to aspects of the present disclosure, a UE reports its machine learning processing capability to the base station. In some aspects, the report indicates a machine learning hardware capability. In other aspects, the report indicates an approximate turnaround time for computing the gradient or weight updates in each of the federated learning rounds. In still other aspects of the present disclosure, the UE reports an approximate turnaround time for computing the gradients or weights, for example, as a function of battery status of the UE.

<FIG> is a block diagram illustrating a federated learning system <NUM> according to aspects of the present disclosure. In some configurations, a base station <NUM> (e.g., gNB) shares a global federated learning model <NUM> with a group of user equipment (UEs) <NUM> (e.g., 620a, 620b, 620c) participating in the federated learning process. In these configurations, the model parameters are optimized by the federated learning system <NUM>. The model parameters w(n) represent biases and weights of the global federated learning model <NUM>, g(n) represents the gradient estimates, where n is a federated learning round index. The initial model parameters are designated as w(<NUM>).

In these configurations, the UEs <NUM> each include a local dataset <NUM> (e.g., 640a, 640b, 640c), a gradient computation block <NUM>, and a gradient compression block <NUM>. In this example, the gradient computation block <NUM> of a second UE 620b is configured to perform a local update through decentralized stochastic gradient descent (SGD). Each of the UEs <NUM> performs some type of training iteration, such as a single stochastic gradient descent step or multiple stochastic gradient descent steps as seen in equation (<NUM>): <MAT> where Fk(w(n)) represents a local loss function for a weight w for the nth federated learning round, and gk(n) represents a local gradient, for the nth federated learning round.

After the UEs <NUM> have completed the local updates <MAT>, the gradient compression block <NUM> may compress the computed gradient vector <MAT> as seen in equation (<NUM>), to obtain the compressed values <MAT>, (e.g., 632a, 632b, 632c), where q() represents a compression function: <MAT>.

The UEs <NUM> feedback the computed compressed gradient vectors <MAT>, (e.g., 632a, 632b, 632c) to the base station <NUM>. This federated learning process includes transmission of the computed compressed gradient vectors <MAT> <NUM> (e.g., 632a, 632b, 632c) from all the UEs <NUM> to the base station <NUM> in each round of the process.

In these configurations, the base station <NUM> includes a gradient averaging block <NUM> configured to average the computed compressed gradient vectors <MAT> <NUM>. Although averaging is shown, other types of aggregation are also contemplated. In addition, a model update block <NUM> is configured to update parameters of the global federated learning model <NUM>. The updated model is then sent to all of the UEs <NUM>. This process repeats until a global federated learning accuracy specification is met (e.g., until a global federated learning algorithm converges). An accuracy specification may refer to a desired accuracy level for local training. For example, an accuracy specification may indicate that a local training loss in each iteration of the federated learning process should drop below a threshold.

This global federated learning algorithm is based on a local loss function Fk(w) as seen in equation (<NUM>): <MAT> where xj represents an input vector to the model, yj represents an output scalar from the model, w is a weight vector of the global federated learning model, and Dk represents a size of the dataset at the kth UE. For example, the input could be a vectorized image and the output could be the detected number (e.g., single scalar).

This global federated learning algorithm is also based on a global loss function F(w) (assuming |Dk| = D) as seen in equation (<NUM>): <MAT>.

An overall goal of this federated learning process is to obtain the optimal parameters for the neural network w* that minimizes the global loss function F(w): <MAT>.

In this federated learning process, local calculations of computed compressed gradient vectors <MAT> <NUM> (e.g., for updating the global federated learning model <NUM>) are gathered from the UEs <NUM>, and an average is computed by the gradient averaging block <NUM> (or another type of aggregate estimate) as follows: <MAT>.

Based on the average gradient g(n), the updated model parameters are transmitted (e.g., broadcast) from the base station <NUM> to the UEs <NUM>. In addition, the model update block <NUM> of the base station <NUM> performs a model update as seen in equation (<NUM>): <MAT> where η represents a learning rate, which is a parameter of the global federated learning model <NUM>.

In each round of a federated learning process, a group of UEs sends back weights or gradient updates within a given time interval after they receive the model from the base station. In one configuration, the group size is ten to twenty UEs. If a UE misses a deadline for sending updates, the weights or gradients will become stale, and the base station will not incorporate the gradient update from that UE in that round of the federated learning process.

If a base station is aware of the machine learning capabilities of the UEs participating in the federated learning process, this information could be useful to the base station. For example, a base station may group the UEs for different federated learning rounds according to machine learning capability. If slower UEs are grouped with faster UEs, the slower UEs will be a bottleneck for the training procedure, adversely impacting a convergence time of the federated learning process. Thus, slower UEs may be grouped with other slower UEs, while fast UEs are grouped with other fast UEs. Moreover, different UEs can be paired together for different rounds of the federated learning training process.

According to aspects of the present disclosure, a UE reports its machine learning processing capability to the base station. This machine learning processing capability report can be in a standardized format. For example, the report may be added to the UE capability report defined in 3GPP TS <NUM>. The standardized format may indicate machine learning hardware capabilities of the UE, such as capabilities of the GPU, NPU, etc..

In aspects of the present disclosure, the report indicates a machine learning hardware capability in terms of standard metrics for the machine learning hardware capability. For example, the report may indicate a number of operations per second or a number of multiply-accumulate (MAC) operations per second, etc. These metrics are fundamental hardware characteristics of the UE and do not change over time.

The hardware characteristics may reflect a best case scenario. Thus, the report may indicate manufacturer specifications, such as tera-operations per second (TOP/s) or tera-multiply accumulate operations per second (TMAC/s). The manufacturer specified hardware capability may be closer to real world performance.

In any event, the reported machine learning hardware capability provides the base station with an approximate training time at the UE side, in order to prepare gradient or weight updates. For example, the base station may decide whether a reporting UE is a fast UE or a slow UE based on the reported machine learning hardware capability. The base station may schedule the UEs according to speed ranges. For example, UEs with a first range of processing capabilities may be included in a first group, while UEs with a second range of processing capabilities may be included in a second group. The processing capability may be a machine learning processing capability, in some implementations.

In other aspects of the present disclosure, the report indicates an approximate or estimated turnaround time for computing the gradient or weight updates in each of the federated learning rounds. The report may indicate a quantized time or an approximate time, for example.

The turnaround time is a function of the UE's machine learning hardware capabilities. The turnaround time is also a function of parameters, such as a type of federated learning process employed or an application associated with the particular federated learning process. The turnaround time may be a function of other parameters, such as a desired accuracy level of the machine learning model and/or the actual type of machine learning model being trained. Other parameters affecting the turnaround time include a learning rate for local training, and/or a number of iterations (e.g., stochastic gradient descent iterations) needed before deriving and sending an update.

A batch size for local training at the UE may also influence the turnaround time. For example, a smaller batch of training data takes less time to process than a larger batch of training data. It is noted that a smaller batch size increases the number of iterations.

According to aspects of the present disclosure, a base station may configure the UE with the above-mentioned parameters for a particular federated learning process. The UE can then assess the amount of time for computing the weight or gradient updates with the knowledge of these parameters, and report the (approximate) turnaround time. For this option, as long as the above noted parameters are fixed for a given federated learning process, the UE refrains from sending an updated report. When the parameters are reconfigured, the UE sends an updated report.

In other aspects of the present disclosure, the UE reports an approximate turnaround time for computing the gradients or weights, for example, as a function of battery status of the UE. For instance, if the UE is in power savings mode, the UE may decide not to participate in federated learning. The lack of participation may be implemented, for example, by setting the turnaround time to infinity. In other aspects, the turnaround time may be set to a large value.

It is noted that reporting machine learning hardware capabilities may be less dynamic than reporting a turnaround time. Moreover, reporting a turnaround time as a function of battery status is more dynamic than reporting the turnaround time more generally.

<FIG> is a timing diagram illustrating reporting of machine learning capabilities, according to aspects of the present disclosure. At time t1, a base station <NUM> receives a machine learning (ML) capability report from a first UE 620a. At time t2, the base station <NUM> receives a machine learning (ML) capability report from a second UE 620b. At time t3, the base station <NUM> receives a machine learning (ML) capability report from a third UE 620c. The machine learning capability reports can indicate a machine learning hardware capability or a machine learning turnaround time, as described previously.

Based on the received machine learning capability reports, the base station <NUM> groups the UEs <NUM> at time t4 and schedules the UEs <NUM> in accordance with the groupings at time t5. In this example, the first UE 620a and the third UE 620c are grouped together as faster UEs. The second UE 620b is in its own group. Accordingly, at time t6, the first UE 620a and the third UE 620c send their updates to the machine learning (ML) model. The updates are computed locally at each UE <NUM>, prior to transmission, and will be aggregated at the base station <NUM> for each round of federated learning. At time t7, the second UE 620b transmits its updates to the UE to be included in this round of federated learning. Due to the grouping of UEs <NUM>, slower UEs may not miss deadlines for their round of federated learning updates. Accordingly, the base station considers a fuller set of updates and can train the model more quickly and accurately.

<FIG> is a flow diagram illustrating an example process <NUM> performed, for example, by a UE, in accordance with various aspects of the present disclosure. The example process <NUM> is an example of user equipment (UE) capability reporting for machine learning applications.

As shown in <FIG>, in some aspects, the process <NUM> includes receiving a machine learning model from a base station (block <NUM>). For example, the UE (e.g., using the antenna <NUM>, DEMOD/MOD <NUM>, MIMO detector <NUM>, receive processor <NUM>, controller/processor <NUM>, and/or memory <NUM>) can receive a machine learning model. The machine learning model is trained in a federated learning process.

The process <NUM> also includes reporting, to the base station, a machine learning processing capability (block <NUM>). For example, the UE (e.g., using the antenna <NUM>, DEMOD/MOD <NUM>, TX MIMO processor <NUM>, transmit processor <NUM>, controller/processor <NUM>, and/or memory <NUM>) can report, to the base station, a machine learning processing capability. In some aspects of the present disclosure, the report indicates a machine learning hardware capability. In other aspects, the report indicates an approximate turnaround time for computing the gradient or weight updates in each of the federated learning rounds. In still other aspects, the UE reports an approximate turnaround time for computing the gradients or weights, for example, as a function of battery status of the UE. This machine learning processing capability report can be in a standardized format.

The process <NUM> further includes transmitting, to the base station, gradient updates or weight updates to the machine learning model (block <NUM>). For example, the UE (e.g., using the antenna <NUM>, DEMOD/MOD <NUM>, TX MIMO processor <NUM>, transmit processor <NUM>, controller/processor <NUM>, and/or memory <NUM>) can transmit, to the base station, gradient updates or weight updates. The updates may be calculated locally as part of a federated learning process.

<FIG> is a flow diagram illustrating an example process <NUM> performed, for example, by a base station, in accordance with various aspects of the present disclosure. The example process <NUM> is an example of user equipment (UE) capability reporting for machine learning applications.

As shown in <FIG>, in some aspects, the process <NUM> includes transmitting a machine learning model to a number of user equipment (UEs) (block <NUM>). For example, the base station (e.g., using the antenna <NUM>, MOD/DEMOD <NUM>, TX MIMO processor <NUM>, transmit processor <NUM>, controller/processor <NUM>, and/or memory <NUM>) can transmit a machine learning model. The machine learning model is trained in a federated learning process.

The process <NUM> includes receiving, from each of the number of UEs, a machine learning processing capability report (block <NUM>). For example, the base station (e.g., using the antenna <NUM>, MOD/DEMOD <NUM>, MIMO detector <NUM>, receive processor <NUM>, controller/processor <NUM>, and/or memory <NUM>) can receive, from each of the number of UEs, a machine learning processing capability report. In some aspects of the present disclosure, the report indicates a machine learning hardware capability. In other aspects, the report indicates an approximate turnaround time for computing the gradient or weight updates in each of the federated learning rounds. In still other aspects, the UE reports an approximate turnaround time for computing the gradients or weights, for example, as a function of battery status of the UE. This machine learning processing capability report can be in a standardized format.

The process <NUM> includes grouping the UEs in accordance with the machine learning processing capability reports, for receiving gradient updates to the machine learning model (block <NUM>). For example, the base station (e.g., using the antenna <NUM>, MOD/DEMOD <NUM>, MIMO detector <NUM>, TX MIMO processor <NUM>, receive processor <NUM>, transmit processor <NUM>, controller/processor <NUM>, and/or memory <NUM>) can group the number of UEs. For example, the base station may decide whether a reporting UE is a fast UE or a slow UE based on the reported machine learning hardware capability. The base station may schedule the UEs according to speed ranges. A set of higher speed UEs may be group together, while a set of slower speed UEs may be grouped together.

As used, the term "component" is intended to be broadly construed as hardware, firmware, and/or a combination of hardware and software. As used, a processor is implemented in hardware, firmware, and/or a combination of hardware and software.

Some aspects are described in connection with thresholds. As used, satisfying a threshold may, depending on the context, refer to a value being greater than the threshold, greater than or equal to the threshold, less than the threshold, less than or equal to the threshold, equal to the threshold, not equal to the threshold, and/or the like.

It will be apparent that systems and/or methods described may be implemented in different forms of hardware, firmware, and/or a combination of hardware and software. Thus, the operation and behavior of the systems and/or methods were described without reference to specific software code-it being understood that software and hardware can be designed to implement the systems and/or methods based, at least in part, on the description.

Claim 1:
A method of wireless communication by a user equipment, UE, comprising:
receiving (<NUM>) a machine learning model trained in a federated learning process from a base station (<NUM>);
reporting (<NUM>), to the base station (<NUM>), a machine learning processing capability indicating a turnaround time for the UE to compute a gradient for the machine learning model;
computing gradient updates or weight updates for the machine learning model in accordance with the turnaround time; and
transmitting (<NUM>), to the base station, the gradient updates or weight updates to the machine learning model.