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Andrew Hard

Andrew Hard

I'm a Software Engineer at Google on the Bard team. I've worked on federated learning, keyword spotting and voice activation technologies, and NLP. I hold a PhD in high-energy physics from the University of Wisconsin, and spent 6 years conducting research at CERN prior to joining Google.
Authored Publications
Google Publications
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    Preview abstract How well do existing federated learning algorithms learn from client devices that return model updates with a significant time delay? Is it even possible to learn effectively from clients that report back minutes, hours, or days after being scheduled? We answer these questions by developing Monte Carlo simulations of client latency that are guided by real-world applications. We compare well-known synchronous optimization algorithms like FedAvg and FedAdam with the state-of-the-art asynchronous FedBuff algorithm, and discover that these existing approaches often struggle to learn from severely delayed clients. To improve upon these, we experiment with modifications including distillation regularization and exponential moving averages of model weights. Finally, we invent two new algorithms, FARe-DUST and FeAST-on-MSG, based on distillation and averaging, respectively. Experiments with the EMNIST, CIFAR-100, and StackOverflow benchmark federated learning tasks demonstrate that our new algorithms outperform existing ones in terms of accuracy for straggler clients, while also providing better trade-offs between training time and total accuracy. View details
    Diurnal or Nocturnal? Federated Learning of Multi-branch Networks from Periodically Shifting Distributions
    Chen Zhu
    Jakub Konečný
    Tom Goldstein
    International Conference on Learning Representations (2022) (to appear)
    Preview abstract Federated learning has been applied to train machine learning models from decentralized client data on mobile devices in practice. The population of the large scale clients are observed to have periodically shifting distributions, which can cause instability in training and degrade the final model performance. In this paper, instead of adopting the block-cyclic distribution shifts in previous papers, we model the population distribution to be a mixture distribution gradually changing between daytime subpopulation and nighttime subpopulation. We verified this intuitive modification better matches the training observation in practical federated learning systems. We propose multi-branch networks to handle the domain differences in subpopulations, and exploit a federated Expectation-Maximization (EM) algorithm with temporal priors to select branches for each client to handle the distribution shift. Experiments for image classification on EMNIST and CIFAR datasets, and next word prediction on the Stack Overflow dataset show that the proposed algorithm can effectively mitigate the impact of the distribution shift and significantly improve the final model performance. View details
    Preview abstract Federated learning (FL) enables learning from decentralized privacy-sensitive data, with computations on raw data confined to take place at edge clients. This paper introduces mixed FL, which incorporates an additional loss term calculated at the coordinating server (while maintaining FL's private data restrictions). There are numerous benefits. For example, additional datacenter data can be leveraged to jointly learn from centralized (datacenter) and decentralized (federated) training data and better match an expected inference data distribution. Mixed FL also enables offloading some intensive computations (e.g., embedding regularization) to the server, greatly reducing communication and client computation load. For these and other mixed FL use cases, we present three algorithms: PARALLEL TRAINING, 1-WAY GRADIENT TRANSFER, and 2-WAY GRADIENT TRANSFER. We state convergence bounds for each, and give intuition on which are suited to particular mixed FL problems. Finally we perform extensive experiments on three tasks, demonstrating that mixed FL can blend training data to achieve an oracle's accuracy on an inference distribution, and can reduce communication and computation overhead by over 90%. Our experiments confirm theoretical predictions of how algorithms perform under different mixed FL problem settings. View details
    A Field Guide to Federated Optimization
    Jianyu Wang
    Gauri Joshi
    Maruan Al-Shedivat
    Galen Andrew
    A. Salman Avestimehr
    Katharine Daly
    Deepesh Data
    Suhas Diggavi
    Hubert Eichner
    Advait Gadhikar
    Antonious M. Girgis
    Filip Hanzely
    Chaoyang He
    Samuel Horvath
    Martin Jaggi
    Tara Javidi
    Sai Praneeth Karimireddy
    Jakub Konečný
    Sanmi Koyejo
    Tian Li
    Peter Richtarik
    Karan Singhal
    Virginia Smith
    Mahdi Soltanolkotabi
    Weikang Song
    Sebastian Stich
    Ameet Talwalkar
    Hongyi Wang
    Blake Woodworth
    Honglin Yuan
    Mi Zhang
    Tong Zhang
    Chunxiang (Jake) Zheng
    Chen Zhu
    arxiv (2021)
    Preview abstract Federated learning and analytics are a distributed approach for collaboratively learning models (or statistics) from decentralized data, motivated by and designed for privacy protection. The distributed learning process can be formulated as solving federated optimization problems, which emphasize communication efficiency, data heterogeneity, compatibility with privacy and system requirements, and other constraints that are not primary considerations in other problem settings. This paper provides recommendations and guidelines on formulating, designing, evaluating and analyzing federated optimization algorithms through concrete examples and practical implementation, with a focus on conducting effective simulations to infer real-world performance. The goal of this work is not to survey the current literature, but to inspire researchers and practitioners to design federated learning algorithms that can be used in various practical applications. View details
    Preview abstract With privacy as a motivation, Federated Learning (FL) is an increasingly used paradigm where learning takes place collectively on edge devices, with user-generated training examples that never leave the device. These on-device training examples are gathered in situ during the course of users’ interactions with their devices, and thus are highly reflective of at least part of the inference data distribution. Yet gaps may still exist, where on-device training examples are lacking for some data inputs expected to be encountered at inference time. This paper proposes a way to mitigate these gaps: selective usage of datacenter data, mixed in with FL. By mixing decentralized (federated) and centralized (datacenter) data, we can form an effective training data distribution that better matches the inference data distribution, resulting in more useful models. View details
    Preview abstract We demonstrate that a production-quality keyword-spotting model can be trained on-device using federated learning and achieve comparable false accept and false reject rates to a centrally-trained model. To overcome the algorithmic constraints associated with fitting on-device data (which are inherently non-independent and identically distributed), we conduct thorough empirical studies of optimization algorithms and hyperparameter configurations using large-scale federated simulations. And we explore techniques for utterance augmentation and data labeling to overcome the physical limitations of on-device training. View details
    Preview abstract We train a recurrent neural network language model using a distributed, on-device learning framework called federated learning for the purpose of next-word prediction in a virtual keyboard for smartphones. Server-based training using stochastic gradient descent is compared with training on client devices using the Federated Averaging algorithm. The federated algorithm, which enables training on a higher-quality dataset for this use case, is shown to achieve better prediction recall. This work demonstrates the feasibility and benefit of training language models on client devices without exporting sensitive user data to servers. The federated learning environment gives users greater control over their data and simplifies the task of incorporating privacy by default with distributed training and aggregation across a population of client devices. View details
    Preview abstract We train a recurrent neural network language model using a distributed, on-device learning framework called federated learning for the purpose of next-word prediction in a virtual keyboard for smartphones. Server-based training using stochastic gradient descent is compared with training on client devices using the FederatedAveraging algorithm. The federated algorithm, which enables training on a higher-quality dataset for this use case, is shown to achieve better prediction recall. This work demonstrates the feasibility and benefit of training language models on client devices without exporting sensitive user data to servers. The federated learning environment gives users greater control over their data and simplifies the task of incorporating privacy by default with distributed training and aggregation across a population of client devices. View details
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