Kurt Partridge
Authored Publications
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Mixed Federated Learning: Joint Decentralized and Centralized Learning
Karan Singhal
Satyen Kale
Arxiv (2022) (to appear)
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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.
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Jointly Learning from Decentralized (Federated) and Centralized Data to Mitigate Distribution Shift
NeurIPS 2021 Workshop on Distribution Shifts (2021) (to appear)
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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.
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Training Keyword Spotting Models on Non-IID Data with Federated Learning
Aishanee Shah
Cameron Nguyen
Niranjan Subrahmanya
Pai Zhu
Interspeech (2020)
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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.
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Effects of Language Modeling and its Personalization on Touchscreen Typing Performance
Andrew Fowler
Proceedings of the SIGCHI Conference on Human Factors in Computing Systems (CHI 2015), ACM, New York, NY, USA, pp. 649-658
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Modern smartphones correct typing errors and learn userspecific
words (such as proper names). Both techniques are useful, yet little has been published about their technical specifics and concrete benefits. One reason is that typing accuracy is difficult to measure empirically on a large scale. We describe a closed-loop, smart touch keyboard (STK) evaluation system that we have implemented to solve this problem. It includes a principled typing simulator for generating human-like noisy touch input, a simple-yet-effective decoder for reconstructing typed words from such spatial data, a large web-scale background language model (LM), and a method for incorporating LM
personalization. Using the Enron email corpus as a personalization test set, we show for the first time at this scale that a combined spatial/language model reduces word error rate from a pre-model baseline of 38.4% down to 5.7%, and that LM personalization can improve this further to 4.6%.
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Making touchscreen keyboards adaptive to keys, hand postures, and individuals: a hierarchical spatial backoff model approach
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Ying Yin
Proceedings of the SIGCHI Conference on Human Factors in Computing Systems (CHI 2013), ACM, New York, NY, pp. 2775-2784
Octopus: Evaluating Touchscreen Keyboard Correction and Recognition Algorithms via “Remulation”
Shiri Azenkot
Proceedings of the SIGCHI Conference on Human Factors in Computing Systems (CHI 2013), ACM, New York, NY, USA, pp. 543-552
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The time and labor demanded by a typical laboratory-based keyboard evaluation are limiting resources for algorithmic adjustment and optimization. We propose Remulation, a complementary method for evaluating touchscreen keyboard correction and recognition algorithms. It replicates prior user study data through real-time, on-device simulation. To demonstrate remulation, we have developed Octopus, an evaluation tool that enables keyboard developers to efficiently measure and inspect the impact of algorithmic changes without conducting resource-intensive user studies. It can also be used to evaluate third-party keyboards in a “black box” fashion, without access to their algorithms or source code. Octopus can evaluate both touch keyboards and word-gesture keyboards. Two empirical examples show that Remulation can efficiently and effectively measure many aspects of touch screen keyboards at both macro and micro levels. Additionally, we contribute two new metrics to measure keyboard accuracy at the word level: the Ratio of Error Reduction (RER) and the Word Score.
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Bimanual gesture keyboard
Proceeding of UIST 2012 – The ACM Symposium on User Interface Software and Technology, ACM, New York, NY, USA, pp. 137-146
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Gesture keyboards represent an increasingly popular way to input text on mobile devices today. However, current gesture keyboards are exclusively unimanual. To take advantage of the capability of modern multi-touch screens, we created a novel bimanual gesture text entry system, extending the gesture keyboard paradigm from one finger to multiple fingers. To address the complexity of recognizing bimanual gesture, we designed and implemented two related interaction methods, finger-release and space-required, both based on a new multi-stroke gesture recognition algorithm. A formal experiment showed that bimanual gesture behaviors were easy to learn. They improved comfort and reduced the physical demand relative to unimanual gestures on tablets. The results indicated that these new gesture keyboards were valuable complements to unimanual gesture and regular typing keyboards.
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