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Ting Dang

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5 papers

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NeurIPS Conference 2025 Conference Paper

E-BATS: Efficient Backpropagation-Free Test-Time Adaptation for Speech Foundation Models

Jiaheng Dong
Hong Jia
Soumyajit Chatterjee
Abhirup Ghosh
James Bailey
Ting Dang

Speech Foundation Models encounter significant performance degradation when deployed in real-world scenarios involving acoustic domain shifts, such as background noise and speaker accents. Test-time adaptation (TTA) has recently emerged as a viable strategy to address such domain shifts at inference time without requiring access to source data or labels. However, existing TTA approaches, particularly those relying on backpropagation, are memory-intensive, limiting their applicability in speech tasks and resource-constrained settings. Although backpropagation-free methods offer improved efficiency, existing ones exhibit poor accuracy. This is because they are predominantly developed for vision tasks, which fundamentally differ from speech task formulations, noise characteristics, and model architecture, posing unique transferability challenges. In this paper, we introduce E-BAT, first Efficient BAckpropagation-free TTA framework designed explicitly for speech foundation models. E-BAT achieves a balance between adaptation effectiveness and memory efficiency through three key components: (i) lightweight prompt adaptation for a forward-pass-based feature alignment, (ii) a multi-scale loss to capture both global (utterance-level) and local distribution shifts (token-level) and (iii) a test-time exponential moving average mechanism for stable adaptation across utterances. Experiments conducted on four noisy speech datasets spanning sixteen acoustic conditions demonstrate consistent improvements, with 4. 1\%--13. 5% accuracy gains over backpropogation-free baselines and 2. 0$\times$–6. 4$\times$ GPU memory savings compared to backpropogation-based methods. By enabling scalable and robust adaptation under acoustic variability, this work paves the way for developing more efficient adaptation approaches for practical speech processing systems in real-world environments.

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NeurIPS Conference 2025 Conference Paper

FlowerTune: A Cross-Domain Benchmark for Federated Fine-Tuning of Large Language Models

Yan Gao
Massimo R. Scamarcia
Javier Fernandez-Marques
Mohammad Naseri
Chong Ng
Dimitris Stripelis
Zexi Li
Tao Shen

Large Language Models (LLMs) have achieved state-of-the-art results across diverse domains, yet their development remains reliant on vast amounts of publicly available data, raising concerns about data scarcity and the lack of access to domain-specific, sensitive information. Federated Learning (FL) presents a compelling framework to address these challenges by enabling decentralized fine-tuning on pre-trained LLMs without sharing raw data. However, the compatibility and performance of pre-trained LLMs in FL settings remain largely under explored. We introduce the FlowerTune LLM Leaderboard, a first-of-its-kind benchmarking suite designed to evaluate federated fine-tuning of LLMs across four diverse domains: general NLP, finance, medical, and coding. Each domain includes federated instruction-tuning datasets and domain-specific evaluation metrics. Our results, obtained through a collaborative, open-source and community-driven approach, provide the first comprehensive comparison across 26 pre-trained LLMs with different aggregation and fine-tuning strategies under federated settings, offering actionable insights into model performance, resource constraints, and domain adaptation. This work lays the foundation for developing privacy-preserving, domain-specialized LLMs for real-world applications.

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NeurIPS Conference 2024 Conference Paper

TinyTTA: Efficient Test-time Adaptation via Early-exit Ensembles on Edge Devices

Hong Jia
Young D. Kwon
Alessio Orsino
Ting Dang
Domenico Talia
Cecilia Mascolo

The increased adoption of Internet of Things (IoT) devices has led to the generation of large data streams with applications in healthcare, sustainability, and robotics. In some cases, deep neural networks have been deployed directly on these resource-constrained units to limit communication overhead, increase efficiency and privacy, and enable real-time applications. However, a common challenge in this setting is the continuous adaptation of models necessary to accommodate changing environments, i. e. , data distribution shifts. Test-time adaptation (TTA) has emerged as one potential solution, but its validity has yet to be explored in resource-constrained hardware settings, such as those involving microcontroller units (MCUs). TTA on constrained devices generally suffers from i) memory overhead due to the full backpropagation of a large pre-trained network, ii) lack of support for normalization layers on MCUs, and iii) either memory exhaustion with large batch sizes required for updating or poor performance with small batch sizes. In this paper, we propose TinyTTA, to enable, for the first time, efficient TTA on constrained devices with limited memory. To address the limited memory constraints, we introduce a novel self-ensemble and batch-agnostic early-exit strategy for TTA, which enables continuous adaptation with small batch sizes for reduced memory usage, handles distribution shifts, and improves latency efficiency. Moreover, we develop the TinyTTA Engine, a first-of-its-kind MCU library that enables on-device TTA. We validate TinyTTA on a Raspberry Pi Zero 2W and an STM32H747 MCU. Experimental results demonstrate that TinyTTA improves TTA accuracy by up to 57. 6\%, reduces memory usage by up to six times, and achieves faster and more energy-efficient TTA. Notably, TinyTTA is the only framework able to run TTA on MCU STM32H747 with a 512 KB memory constraint while maintaining high performance.

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JBHI Journal 2024 Journal Article

Uncertainty-Aware Health Diagnostics via Class-Balanced Evidential Deep Learning

Tong Xia
Ting Dang
Jing Han
Lorena Qendro
Cecilia Mascolo

Uncertainty quantification is critical for ensuring the safety of deep learning-enabled health diagnostics, as it helps the model account for unknown factors and reduces the risk of misdiagnosis. However, existing uncertainty quantification studies often overlook the significant issue of class imbalance, which is common in medical data. In this paper, we propose a class-balanced evidential deep learning framework to achieve fair and reliable uncertainty estimates for health diagnostic models. This framework advances the state-of-the-art uncertainty quantification method of evidential deep learning with two novel mechanisms to address the challenges posed by class imbalance. Specifically, we introduce a pooling loss that enables the model to learn less biased evidence among classes and a learnable prior to regularize the posterior distribution that accounts for the quality of uncertainty estimates. Extensive experiments using benchmark data with varying degrees of imbalance and various naturally imbalanced health data demonstrate the effectiveness and superiority of our method. Our work pushes the envelope of uncertainty quantification from theoretical studies to realistic healthcare application scenarios. By enhancing uncertainty estimation for class-imbalanced data, we contribute to the development of more reliable and practical deep learning-enabled health diagnostic systems.

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NeurIPS Conference 2021 Conference Paper

COVID-19 Sounds: A Large-Scale Audio Dataset for Digital Respiratory Screening

Tong Xia
Dimitris Spathis
Chlo{\"e} Brown
J Ch
Andreas Grammenos
Jing Han
Apinan Hasthanasombat
Erika Bondareva

Audio signals are widely recognised as powerful indicators of overall health status, and there has been increasing interest in leveraging sound for affordable COVID-19 screening through machine learning. However, there has also been scepticism regarding the initial efforts, due to perhaps the lack of reproducibility, large datasets and transparency which unfortunately is often an issue with machine learning for health. To facilitate the advancement and openness of audio-based machine learning for respiratory health, we release a dataset consisting of 53, 449 audio samples (over 552 hours in total) crowd-sourced from 36, 116 participants through our COVID-19 Sounds app. Given its scale, this dataset is comprehensive in terms of demographics and spectrum of health conditions. It also provides participants' self-reported COVID-19 testing status with 2, 106 samples tested positive. To the best of our knowledge, COVID-19 Sounds is the largest multi-modal dataset of COVID-19 respiratory sounds: it consists of three modalities including breathing, cough, and voice recordings. Additionally, in this paper, we report on several benchmarks for two principal research tasks: respiratory symptoms prediction and COVID-19 prediction. For these tasks we demonstrate performance with a ROC-AUC of over 0. 7, confirming both the promise of machine learning approaches based on these types of datasets as well as the usability of our data for such tasks. We describe a realistic experimental setting that hopes to pave the way to a fair performance evaluation of future models. In addition, we reflect on how the released dataset can help to scale some existing studies and enable new research directions, which inspire and benefit a wide range of future works.

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