Multi-Resource Allocation for On-Device Distributed Federated Learning Systems

This work poses a distributed multi-resource allocation scheme for minimizing the weighted sum of latency and energy consumption in the on-device distributed federated learning (FL) system. Each mobile device in the system engages the model training process within the specified area and allocates its computation and communication resources for deriving and uploading parameters, respectively, to minimize the objective of system subject to the computation/communication budget and a target latency requirement. In particular, mobile devices are connect via wireless TCP/IP architectures. Exploiting the optimization problem structure, the problem can be decomposed to two convex sub-problems. Drawing on the Lagrangian dual and harmony search techniques, we characterize the global optimal solution by the closed-form solutions to all sub-problems, which give qualitative insights to multi-resource tradeoff. Numerical simulations are used to validate the analysis and assess the performance of the proposed algorithm.

Measure-Theoretic Probability of Complex Co-occurrence and E-Integral

Complex high-dimensional co-occurrence data are increasingly popular from a complex system of interacting physical, biological and social processes in discretely indexed modifiable areal units or continuously indexed locations of a study region for landscape-based mechanism. Modeling, predicting and interpreting complex co-occurrences are very general and fundamental problems of statistical and machine learning in a broad variety of real-world modern applications. Probability and conditional probability of co-occurrence are introduced by being defined in a general setting with set functions to develop a rigorous measure-theoretic foundation for the inherent challenge of data sparseness. The data sparseness is a main challenge inherent to probabilistic modeling and reasoning of co-occurrence in statistical inference. The behavior of a class of natural integrals called E-integrals is investigated based on the defined conditional probability of co-occurrence. The results on the properties of E-integral are presented. The paper offers a novel measure-theoretic framework where E-integral as a basic measure-theoretic concept can be the starting point for the expectation functional approach preferred by Whittle (1992) and Pollard (2001) to the development of probability theory for the inherent challenge of co-occurrences emerging in modern high-dimensional co-occurrence data problems and opens the doors to more sophisticated and interesting research in complex high-dimensional co-occurrence data science.

LEAVES: Learning Views for Time-Series Data in Contrastive Learning

Contrastive learning, a self-supervised learning method that can learn representations from unlabeled data, has been developed promisingly. Many methods of contrastive learning depend on data augmentation techniques, which generate different views from the original signal. However, tuning policies and hyper-parameters for more effective data augmentation methods in contrastive learning is often time and resource-consuming. Researchers have designed approaches to automatically generate new views for some input signals, especially on the image data. But the view-learning method is not well developed for time-series data. In this work, we propose a simple but effective module for automating view generation for time-series data in contrastive learning, named learning views for time-series data (LEAVES). The proposed module learns the hyper-parameters for augmentations using adversarial training in contrastive learning. We validate the effectiveness of the proposed method using multiple time-series datasets. The experiments demonstrate that the proposed method is more effective in finding reasonable views and performs downstream tasks better than the baselines, including manually tuned augmentation-based contrastive learning methods and SOTA methods.

Empirical Evaluation of Data Augmentations for Biobehavioral Time Series Data with Deep Learning

Deep learning has performed remarkably well on many tasks recently. However, the superior performance of deep models relies heavily on the availability of a large number of training data, which limits the wide adaptation of deep models on various clinical and affective computing tasks, as the labeled data are usually very limited. As an effective technique to increase the data variability and thus train deep models with better generalization, data augmentation (DA) is a critical step for the success of deep learning models on biobehavioral time series data. However, the effectiveness of various DAs for different datasets with different tasks and deep models is understudied for biobehavioral time series data. In this paper, we first systematically review eight basic DA methods for biobehavioral time series data, and evaluate the effects on seven datasets with three backbones. Next, we explore adapting more recent DA techniques (i.e., automatic augmentation, random augmentation) to biobehavioral time series data by designing a new policy architecture applicable to time series data. Last, we try to answer the question of why a DA is effective (or not) by first summarizing two desired attributes for augmentations (challenging and faithful), and then utilizing two metrics to quantitatively measure the corresponding attributes, which can guide us in the search for more effective DA for biobehavioral time series data by designing more challenging but still faithful transformations. Our code and results are available at Link.

Improving the Sample Efficiency of Prompt Tuning with Domain Adaptation

Prompt tuning, or the conditioning of a frozen pretrained language model (PLM) with soft prompts learned from data, has demonstrated impressive performance on a wide range of NLP tasks. However, prompt tuning requires a large training dataset to be effective and is outperformed by finetuning the entire PLM in data-scarce regimes. Previous work \citep{gu-etal-2022-ppt,vu-etal-2022-spot} proposed to transfer soft prompts pretrained on the source domain to the target domain. In this paper, we explore domain adaptation for prompt tuning, a problem setting where unlabeled data from the target domain are available during pretraining. We propose bOosting Prompt TunIng with doMain Adaptation (OPTIMA), which regularizes the decision boundary to be smooth around regions where source and target data distributions are similar. Extensive experiments demonstrate that OPTIMA significantly enhances the transferability and sample-efficiency of prompt tuning compared to strong baselines. Moreover, in few-shot settings, OPTIMA exceeds full-model tuning by a large margin.

FedOBD: Opportunistic Block Dropout for Efficiently Training Large-scale Neural Networks through Federated Learning

Large-scale neural networks possess considerable expressive power. They are well-suited for complex learning tasks in industrial applications. However, large-scale models pose significant challenges for training under the current Federated Learning (FL) paradigm. Existing approaches for efficient FL training often leverage model parameter dropout. However, manipulating individual model parameters is not only inefficient in meaningfully reducing the communication overhead when training large-scale FL models, but may also be detrimental to the scaling efforts and model performance as shown by recent research. To address these issues, we propose the Federated Opportunistic Block Dropout (FedOBD) approach. The key novelty is that it decomposes large-scale models into semantic blocks so that FL participants can opportunistically upload quantized blocks, which are deemed to be significant towards training the model, to the FL server for aggregation. Extensive experiments evaluating FedOBD against five state-of-the-art approaches based on multiple real-world datasets show that it reduces the overall communication overhead by more than 70% compared to the best performing baseline approach, while achieving the highest test accuracy. To the best of our knowledge, FedOBD is the first approach to perform dropout on FL models at the block level rather than at the individual parameter level.

Bias Reducing Multitask Learning on Mental Health Prediction

There has been an increase in research in developing machine learning models for mental health detection or prediction in recent years due to increased mental health issues in society. Effective use of mental health prediction or detection models can help mental health practitioners re-define mental illnesses more objectively than currently done, and identify illnesses at an earlier stage when interventions may be more effective. However, there is still a lack of standard in evaluating bias in such machine learning models in the field, which leads to challenges in providing reliable predictions and in addressing disparities. This lack of standards persists due to factors such as technical difficulties, complexities of high dimensional clinical health data, etc., which are especially true for physiological signals. This along with prior evidence of relations between some physiological signals with certain demographic identities restates the importance of exploring bias in mental health prediction models that utilize physiological signals. In this work, we aim to perform a fairness analysis and implement a multi-task learning based bias mitigation method on anxiety prediction models using ECG data. Our method is based on the idea of epistemic uncertainty and its relationship with model weights and feature space representation. Our analysis showed that our anxiety prediction base model introduced some bias with regards to age, income, ethnicity, and whether a participant is born in the U.S. or not, and our bias mitigation method performed better at reducing the bias in the model, when compared to the reweighting mitigation technique. Our analysis on feature importance also helped identify relationships between heart rate variability and multiple demographic groupings.

NICO++: Towards Better Benchmarking for Domain Generalization

Despite the remarkable performance that modern deep neural networks have achieved on independent and identically distributed (I.I.D.) data, they can crash under distribution shifts. Most current evaluation methods for domain generalization (DG) adopt the leave-one-out strategy as a compromise on the limited number of domains. We propose a large-scale benchmark with extensive labeled domains named NICO++ along with more rational evaluation methods for comprehensively evaluating DG algorithms. To evaluate DG datasets, we propose two metrics to quantify covariate shift and concept shift, respectively. Two novel generalization bounds from the perspective of data construction are proposed to prove that limited concept shift and significant covariate shift favor the evaluation capability for generalization. Through extensive experiments, NICO++ shows its superior evaluation capability compared with current DG datasets and its contribution in alleviating unfairness caused by the leak of oracle knowledge in model selection.

Semi-Supervised Learning and Data Augmentation in Wearable-based Momentary Stress Detection in the Wild

Physiological and behavioral data collected from wearable or mobile sensors have been used to estimate self-reported stress levels. Since the stress annotation usually relies on self-reports during the study, a limited amount of labeled data can be an obstacle in developing accurate and generalized stress predicting models. On the other hand, the sensors can continuously capture signals without annotations. This work investigates leveraging unlabeled wearable sensor data for stress detection in the wild. We first applied data augmentation techniques on the physiological and behavioral data to improve the robustness of supervised stress detection models. Using an auto-encoder with actively selected unlabeled sequences, we pre-trained the supervised model structure to leverage the information learned from unlabeled samples. Then, we developed a semi-supervised learning framework to leverage the unlabeled data sequences. We combined data augmentation techniques with consistency regularization, which enforces the consistency of prediction output based on augmented and original unlabeled data. We validated these methods using three wearable/mobile sensor datasets collected in the wild. Our results showed that combining the proposed methods improved stress classification performance by 7.7% to 13.8% on the evaluated datasets, compared to the baseline supervised learning models.

More to Less (M2L): Enhanced Health Recognition in the Wild with Reduced Modality of Wearable Sensors

Accurately recognizing health-related conditions from wearable data is crucial for improved healthcare outcomes. To improve the recognition accuracy, various approaches have focused on how to effectively fuse information from multiple sensors. Fusing multiple sensors is a common scenario in many applications, but may not always be feasible in real-world scenarios. For example, although combining bio-signals from multiple sensors (i.e., a chest pad sensor and a wrist wearable sensor) has been proved effective for improved performance, wearing multiple devices might be impractical in the free-living context. To solve the challenges, we propose an effective more to less (M2L) learning framework to improve testing performance with reduced sensors through leveraging the complementary information of multiple modalities during training. More specifically, different sensors may carry different but complementary information, and our model is designed to enforce collaborations among different modalities, where positive knowledge transfer is encouraged and negative knowledge transfer is suppressed, so that better representation is learned for individual modalities. Our experimental results show that our framework achieves comparable performance when compared with the full modalities. Our code and results will be available at https://github.com/compwell-org/More2Less.git.