Applied Federated Learning: Improving Google Keyboard Query Suggestions

12/07/2018 ∙ by Timothy Yang, et al. ∙ 0

Federated learning is a distributed form of machine learning where both the training data and model training are decentralized. In this paper, we use federated learning in a commercial, global-scale setting to train, evaluate and deploy a model to improve virtual keyboard search suggestion quality without direct access to the underlying user data. We describe our observations in federated training, compare metrics to live deployments, and present resulting quality increases. In whole, we demonstrate how federated learning can be applied end-to-end to both improve user experiences and enhance user privacy.

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1 Introduction

The introduction of Federated Learning (FL) [1, 2, 3] enables a new paradigm of machine learning where both the training data and most of the computation involved in model training are decentralized. In contrast to traditional server-side training where user data is aggregated on centralized servers for training, FL instead trains models on end user devices while aggregating only ephemeral parameter updates on a centralized server. This is particularly advantageous for environments where privacy is paramount.

The Google Keyboard (Gboard) is a virtual keyboard for mobile devices with over 1 billion installs in 2018. Gboard includes both typing features like text autocorrection, next-word prediction and word completions as well as expression features like emoji, GIFs and Stickers (curated, expressive illustrations and animations). As both a mobile application and keyboard, Gboard has unique constraints which lends itself well to both on-device inference and training. First, as a keyboard application with access to much of what a user types into their mobile device, Gboard must respect the user’s privacy. Using FL allows us to train machine learning models without collecting sensitive raw input from users. Second, latency must be minimal; in a mobile typing environment, timely suggestions are necessary in order to maintain relevance. On-device inference and training through FL enable us to both minimize latency and maximize privacy.

In this paper, we use FL in a commercial, global-scale setting to train and deploy a model to production for inference – all without access to the underlying user data. Our use case is search query suggestions [4]: when a user enters text, Gboard uses a baseline model to determine and possibly surface search suggestions relevant to the input. For instance, typing “Let’s eat at Charlie’s” may display a web query suggestion to search for nearby restaurants of that name; other types of suggestions include GIFs and Stickers. Here, we improve the feature by filtering query suggestions from the baseline model with an additional triggering model that is trained with FL. By combining query suggestions with FL and on-device inference, we demonstrate quality improvements to Gboard suggestions while enhancing user privacy and respecting mobile constraints.

This is just one application of FL – one where developers have never had access to the training data. Other works have additionally explored federated multi-task learning [5], parallel stochastic optimization [6], and threat actors in the context of FL [7]. For next word prediction, Gboard has also used FL to train a neural language model which demonstrated better performance than a model trained with traditional server-based collection and training [8]. Language models have also been trained with FL and differential privacy for further privacy enhancements [9, 10]. By leveraging federated learning, we continue to improve user experience in a privacy-advantaged manner.

This paper is organized as follows. In Section 2 we introduce FL, its advantages and the enabling system infrastructure. Section 3 describes the trained and deployed model architecture. Section 4 dives into our experience training models with FL including training requirements and characteristics. In Section 5, we deploy the federated trained model in a live inference experiment and discuss the results, especially with respect to expected versus actual metrics.

2 Federated Learning and On-device Infrastructure

2.1 Federated Learning

The wealth of user interaction data on mobile devices, including typing, gestures, video and audio capture, etc., holds the promise of enabling ever more intelligent applications. FL enables development of such intelligent applications while simplifying the task of building privacy into infrastructure and training.

FL is an approach to distributed computation in which the data is kept at the network edges and never collected centrally [1]. Instead, minimal, focused model updates are transmitted, optionally employing additional privacy-preserving technologies such as secure multiparty computation [11] and differential privacy [10, 12, 13]. Compared to traditional approaches in which data is collected and stored in a central location, FL offers increased privacy.

In summary, FL is best suited for tasks where one or more of the following hold:

  1. The task labels don’t require human labelers but are naturally derived from user interaction.

  2. The training data is privacy sensitive.

  3. The training data is too large to be feasibly collected centrally.

In particular, the best tasks for FL are those where (1) applies, and additionally (2) and/or (3) apply.

2.2 Privacy Advantages of Federated Learning

FL, specifically Federated Averaging [1], in its most basic form proceeds as follows. In a series of rounds, the parameter server selects some number of clients

to participate in training for that round. Each selected client downloads a copy of the current model parameters and performs some number of local model updates using its local training data; for example, it may perform a single epoch of minibatch stochastic gradient descent. Then the clients upload their model update – that is, the difference between the final parameters after training and the original parameters – and the server averages the contributions before accumulating them into the global model.

In contrast to uploading the training data to the server, the FL approach has clear privacy advantages even in its most basic form:

  1. Only the minimal information necessary for model training (the model parameter deltas) is transmitted. The updates will never contain more information than the data from which they derive, and typically will contain much less. In particular, this reduces the risk of deanonymization via joins with other data [14].

  2. The model update is ephemeral, lasting only long enough to be transmitted and incorporated into the global model. Thus while the model aggregator needs to be trusted enough to be given access to each client’s model parameter deltas, only the final, trained model is supplied to end users for inference. Typically any one client’s contribution to that final model is negligible.

In addition to these advantages, FL can guarantee an even higher standard of privacy by making use of two additional techniques. With secure aggregation [11], clients’ updates are securely summed into a single aggregate update without revealing any client’s individual component even to the server. This is accomplished by cryptographically simulating a trusted third party. Differential privacy techniques can be used in which each client adds a carefully calibrated amount of noise to their update to mask their contribution to the learned model [10]. However, since neither of these techniques were employed in the present work, we will not describe them in further detail here.

2.3 System Description

Figure 1: Architecture overview. Inference and training are on-device; model updates are sent to the server during training rounds and trained models are deployed manually to clients.

In this section we provide a brief technical description of the client and server side runtime that enables FL in Gboard by walking through the process of performing training, evaluation and inference of the query suggestion triggering model.

As described earlier, our use case is to train a model that predicts whether query suggestions are useful, in order to filter out less relevant queries. We collect training data for this model by observing user interactions with the app: when surfacing a query suggestion to a user, a tuple (features; label) is stored in an on-device training cache, a SQLite based database with a time-to-live based data retention policy.

  • features is a collection of query and context related information

  • label is the associated user action from {clicked, ignored}.

This data is then used for on-device training and evaluation of models provided by our servers.

A key requirement for our on-device machine learning infrastructure is to have no impact on user experience and mobile data usage. We achieve this by using Android’s JobScheduler to schedule background jobs that run in a separate Unix process when the device is idle, charging, and connected to an unmetered network, and interrupt the task when these conditions change.

When conditions allow – typically at night time when a phone is charging and connected to a Wi-Fi network – the client runtime is started and checks in with our server infrastructure, providing a population name identifier, but no information that could be used to identify the device or user. The server runtime waits until a predefined number of clients for this population have connected, then provides each with a training task that contains:

  • a model consisting of a TensorFlow graph and checkpoint 

    [15]

  • metadata about how to execute the model (input + output node names, types and shapes; operational metrics to report to the server such as loss, statistics of the data processed). Execution can refer, but is not limited to, training or evaluation passes.

  • selection criteria used to query the training cache (e.g. filter data by date)

The client executes the task using a custom task interpreter based on TensorFlow Mobile [16], a stripped down Android build of the TensorFlow runtime. In the case of training, a task-defined number of epochs (stochastic gradient descent passes over the training data) are performed, and the resulting updates to the model and operational metrics are anonymously uploaded to the server. There – again using TensorFlow – these ephemeral updates are aggregated using the Federated Averaging algorithm to produce a new model, and the aggregate metrics allow for monitoring training progress.

To balance the load across more devices and avoid over- or under-representing individual devices in the training procedure, the client receives a minimum delay it should wait before checking in with the server again. In parallel to these on-device training tasks that modify the model, we also execute on-device evaluation tasks of the most recent model iteration similar to data center training to monitor progress, and estimate click-threshold satisfying requirements such as retained impression rate. Upon convergence, a trained checkpoint is used to create and deploy a model to clients for inference.

3 Model Architecture

Figure 2: Setup of the baseline model (traditionally server trained) with the triggering model (federated trained). The baseline model generates candidates and the triggering model decides whether to show the candidate.

In this section, we describe the model setup used to improve Gboard’s query suggestion system. The system works in two stages – a traditionally server-side trained baseline model which generates query candidates, and a triggering model trained via FL (Figure 2). The goal is to improve query click-through-rate (CTR) by taking suggestions from the baseline model and removing low quality suggestions through the triggering model.

3.1 Baseline Model

First, we use a baseline model

for query suggestion, trained offline with traditional server-based machine learning techniques. This model first generates query suggestion candidates by matching the user’s input to an on-device subset of the Google Knowledge Graph (KG) 

[17].

It then scores these suggestions using a Long Short-Term Memory (LSTM) 

[18] network trained on an offline corpus of chat data to detect potential query candidates. This LSTM is trained to predict the KG category of a word in a sentence and returns higher scores when the KG category of the query candidate matches the expected category. With on-device training, we expect to improve over the baseline model by making use of user clicks and interactions – signals which are available on-device for federated training.

The highest scoring candidate from the baseline model is selected and displayed as a query suggestion (an impression). The user then either clicks on or ignores the suggestion. We store these suggestions and user interactions in the on-device training cache, along with other features like time of day, to generate training examples for use in FL.

3.2 Triggering Model

The task of the federated trained model is designed to take in the suggested query candidate from the baseline model, and determine if the suggestion should or should not be shown to the user. We refer to this model as the triggering model

. The triggering model used in our experiments is a logistic regression model trained to predict the probability of a click; the output is a score for a given query, with higher scores meaning greater confidence in the suggestion.

When deploying the triggering model, we select a threshold

in order to reach a desired triggering rate, where a higher threshold is stricter and reduces the trigger rate of suggestions. Tuning the triggering threshold allows us to balance the tradeoff between providing value to the user and potentially hiding a valuable suggestion. As a logistic regression model, the score is in logit space, where the predicted probability of a click is the logistic sigmoid function applied to the score. In model training and deployment, we evaluated performance at uniformly spaced thresholds.

A feature-based representation of the query is supplied to the logistic regression model. Below are some of the features we incorporate in our model:

Past Clicks and Impressions The number of impressions the current user has seen on past rich suggestions, as well as the number of times they have clicked on a suggestion, both represented as log transformed real values. The model takes in both overall clicks and impressions, as well as clicks and impressions broken down by KG category. This allows the model to personalize triggering based on past user behavior.

Baseline Score The score output by the baseline model, represented as a binned, real-valued feature. This score is derived from an LSTM model over the input text, which incorporates context into the model.

Day of Week, Hour of Day

These temporal features, represented as one-hot vectors, allow the model to capture temporal patterns in query suggestion click behavior.

Using a logistic regression model as an initial use case of FL has the advantage of being easily trainable given the convexity of the error function, as compared to multi-layer neural networks. For our initial training, we have a limited number of clients and training examples, which makes it impractical to train models with a large number of parameters. Furthermore, the label is binary and heavily skewed (we have many more impressions than clicks). However a benefit of logistic regression is that it is possible to interpret and validate the resulting trained model by directly inspecting the model weights. In other environments with more data or clients, more complex neural network models can be trained with FL 

[8].

4 Training with Federated Learning

Here we describe the conditions we require for FL, as well as observations from training with FL.

4.1 Federated Training Requirements

For training with FL, we enforce several constraints on both devices and FL tasks.


To participate in training, a client device must meet the following requirements:

Environmental Conditions Device must be charging, on unmetered network (typically Wi-Fi), and idle. This primarily translates to devices being charged overnight and minimizes impact to the user experience.

Device Specifications Device must have at least 2GB of memory and operate Android SDK level 21+.

Language Restriction Limited to en-US and en-CA users. However, many of our training clients are in India and other countries which commonly default to an Android locale of en-US, causing unexpected training populations. While this is fixed in later infrastructure iterations, the work presented here does have this skew.


During federated training, we apply the following server constraints to our FL tasks:

Goal Client Count The target number of clients for a round of federated training, here 100.

Minimum Client Count The minimum number of clients required to run a round. Here 80, i.e. although we ideally want 100 training clients, we will run a round even if we only have 80 clients.

Training Period How frequently we would like to run rounds of training. Here 5 min.

Report Window The maximum time to wait for clients to report back with model updates, here 2 minutes.

Minimum Reporting Fraction The fraction of clients, relative to the actual number of clients gathered for a round, which have to report back to commit a round by the end of the Report Window. Here 0.8.

4.2 Federated Training

Figure 3: Round completion over time and round completion rate over time, times are in PST. Rounds progress faster at night when more devices are charging and on an unmetered network.
Figure 4: Eval loss and training example count over time, times are in PST, hour ranges inclusive. Training example count is highest in the evening as more devices are available. In contrast, eval loss is highest during the day when few devices are available and those available represent a skewed population.
Figure 5: Train and eval loss of the logistic regression triggering model over rounds (bucketed to 100 rounds).
Figure 6: Retained impressions at thresholds , uniformly spaced, over time.

From our training of the Triggering Model with FL, we make several observations.

Since we only train on-device when the device is charging, connected to an unmetered network, and idle, most training occurs in the evening hours, leading to diurnal patterns. As a result, training rounds progress much more quickly at night than during the day as more clients are available, where night/day are in North America time zones since we are targeting devices with country United States and Canada.

Figure 3 shows training progress across a week (times in PST) while Figure 4 shows our model training and eval loss bucketed by time of day. Note that most round progression is centered around midnight, while round completion around noon is nearly flat. While there are still clients available during non-peak hours, fewer rounds are completed due to fewer available clients as well as client contention from running other training tasks, e.g. to train language models [8]. As a result, it is more difficult to satisfy the training task parameters – in our case 100 clients with 80% reporting back within two minutes for a successful round.

Furthermore, devices which are training during off-peak hours (during the day in North America) have an inherent skew. For example, devices which require charging during the day may indicate that the phone is an older device with a battery which requires more frequent charging.

Similarly, our country restriction was based on the device’s Android country so any device with a locale set to en-US or en-CA would participate in training even if the user was not geographically located in the US or Canada. In particular, many devices in and around India are set by default to Android locale en-US and are not modified by end-users; as a result, many of our devices training during the day (PST) are devices located in and around India (where it is evening). These developing markets tend to have less reliable network and power access which contributes to low round completions [19, 20, 21].

The diurnal distribution of user populations also contributes to regular cycles in model metrics, including training and eval loss, during federated training. Since the devices training on clients during the day are often unexpected user populations with bias from the main population, the overall model loss tends to increase during the day and decrease at night. Both of these diurnal effects are evident in Figure 5 which depicts the average eval loss and the average training examples per round, bucketed by time of day. Note that training example count is highest in the evening as more devices are available. In contrast, eval loss is highest during the day when few devices are available and those available represent a skewed population.

In addition to the loss, we can measure other metrics to track the performance of our model during training. In Figure 6, we plot the retained impressions at various thresholds over time.

As a whole, training with FL introduces a number of interesting diurnal characteristics. We expect that as development of FL continues, overall training speed of FL will increase; however, the nature of globally-distributed training examples and clients will continue to be inherent challenges with FL.

4.3 Model Debugging Without Training Example Access

Typically a trained machine learning model can be validated by evaluating its performance on a held-out validation set. The FL analogue to such a central validation set is to perform evaluation by pushing the trained model to devices to score it on their own data and report back the results. However, on-device metrics like loss, CTR and retained impressions do not necessarily tell the whole story that traditionally inspecting key training examples might provide. For this reason it is valuable to have tools for debugging models without reference to training data.

During model development, synthetically generated proxy data was used to validate the model architecture to select ballparks for basic hyperparameters like learning rate. The generated data encoded some basic first-order statistics like aggregate click-through-rate, an expectation that some users would click more than others or at different times of day, etc. The model was also validated with integration and end-to-end testing on a handful of realistic hand – constructed and donated examples. The combination of synthetic and donated data enabled model development, validated that the approach learned patterns we’d encoded into the data, and built confidence that it would learn as expected with data generated by the application.

To validate the model after training, we interpreted the coefficients of our logistic regression model via direct examination of the weights in order to gain insight into what the model had learned. We determined that FL had produced a reasonable model (reasonable enough to warrant pushing to devices for live inference experiments) considering that:

  • the weights corresponding to query categories had intuitive values

  • the weights corresponding to binned real-valued features tended to have smooth, monotone progressions

  • the weights of more common features were larger in absolute value, i.e. the model came to rely on them more due to their frequency.

Manual inspection of the weights also uncovered an unusual pattern that revealed a way to improve future model iterations. One binned real-valued feature had zero weight for most of its range, indicating that the expected range of the feature was too large. We improved future iterations of the model by restricting the feature to the correct range so the binned values (which did not change in number) gave more precision within the range. This is just one example approach to the broader domain of debugging without training example access.

5 Live Results and Observations

Training Metrics From Federated Training Live Metrics From Live Experiments
Threshold CTR Retained impressions Retained clicks CTR Retained impressions Retained clicks
+3.01% 93.44% 96.25% N/A N/A N/A
+17.40% 75.60% 88.76% +14.52% 67.33% 77.11%
+42.19% 28.45% 40.45% +33.95% 24.18% 32.39%
Table 1: Metrics during training and live model deployment, where , uniformly spaced. A greater threshold indicates a stricter quality bar for suggestions.
Model Live CTR Live Retained Clicks
Model Iteration 1 +14.52% 77.11%
Model Iteration 2 +25.56% 63.39%
Model Iteration 3 +51.49% 82.01%
Table 2: Change in CTR over several trained and deployed models. In later development we trained an LSTM model which performed the best in terms of CTR and Retained Clicks.

After training and sanity checking our logistic regression model, we deployed the model to live users. A model checkpoint from the server was used to build and deploy an on-device inference model that uses the same featurization flow which originally logged training examples on-device. The model outputs a score, which is then compared to a threshold to determine if the suggestion is shown or hidden. By selecting various thresholds, we experiment with different operating points, trading off CTR for retained impressions and clicks. We deployed our models on a population with the same locale restrictions as our training population (en-US and en-CA).

Comparing our expected CTR in training metrics to our actual CTR in live experiments, our live deployments reflect a successful improvement in CTR (Table 1). However, while we capture a majority of the expected improvements, we do observe a slight drop between expected and actual CTR.

Some hypotheses for this CTR follow:

Environmental Conditions Since we require devices to be charging and on unmetered networks, this biases our training towards devices and users with these conditions available. In particular, many users in developing countries do not have reliable access to either stable power or stable unmetered networks [20, 19].

Device Specifications We restricted device training to devices with 2GB of RAM, however our deployment was on all devices without a minimum RAM requirement. This causes skew in the training vs deployment population in terms of device specifications.

Successful Training Clients Recall that our federated training configuration only requires 80% of selected devices to respond in order to close a round. This skews our training population towards higher end devices with more stable networks, as lower end devices are more unstable from a device and network perspective.

Evaluation and Training Client Overlap With our federated training infrastructure, the client selected for training and eval rounds are not necessarily mutually exclusive. However, given that training and eval rounds only select a small subset of the overall training population, we expect the overlap to be 0.1% and have minimal impact on performance skew.

In addition to the above hypotheses, more general sources of skew may also apply, such as model drift due to training and deploy time offsets. As FL continues to mature, we expect that the delta between expected and actual metrics will narrow over time.

The results detailed here were only the first in a sequence of models trained, evaluated, and launched with FL. Successive iterations differed in that they were trained longer on more users’ data, had better tuned hyperparameters, and incorporated additional features. The results of these successive iterations are shown in Table 2, but we do not describe in depth here all the changes made between these iterations. One noteworthy addition in the final model was the inclusion of an LSTM-based featurization of the typed text, which was co-trained with the rest of the logistic regression model, allowing the model to better understand the typed context. Our iterations with FL have demonstrated the ability to develop effective models in a privacy advantaged manner without direct access to the underlying user data.

6 Conclusion

In this work, we applied FL to train, evaluate and deploy a logistic regression model without access to underlying user data to improve keyboard search suggestion quality. We discussed observations about federated learning including the cyclic nature of training, model iteration without direct access to training data, and sources of skew between federated training and live deployments. This training and deployment is one of the first end-to-end examples of FL in a production environment, and explores a path where FL can be used to improve user experience in a privacy-advantaged manner.

7 Acknowledgments

The authors would like to thank colleagues on the Gboard and Google AI team for providing the federated learning framework and for many helpful discussions.

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