Tianren Zhou

Arrhythmia diagnosis using electrocardiogram (ECG) is critical for preventing cardiovascular risks. However, existing deep learning-based methods struggle with label scarcity and contrastive learning-based methods suffer from false-negative samples, which lead to poor model generalization. Besides, due to inter-subject variability, pre-trained models cannot achieve evenly performance across individuals. Conducting model fine-tuning for each individual is computationally expensive and does not guarantee improvement. We propose DiffECG, a diffusion-based self-supervised learning framework for label-efficient and personalized arrhythmia detection. Our method utilizes a diffusion model to extract robust ECG representations, coupled with a novel feature extractor and a multi-modal feature fusion strategy to obtain a well-generalized model. Moreover, we propose an efficient model personalization mechanism based on zeroth-order optimization. It personalizes the model by tuning the noise-adding step t in the diffusion process, significantly reducing computational costs compared to model fine-tuning. Experimental results show that our proposed method outperforms the SOTA method by 37.9% and 23.9% in generalization and personalization performance, respectively. The source code is available at: https://github.com/Auguuust/DiffEC

Federated learning (FL) has been widely adopted in IoT-enabled health monitoring on biosignals thanks to its advantages in data privacy preservation. However, the global model trained from FL generally performs unevenly across subjects since biosignal data is inherent with complex temporal dynamics. The morphological characteristics of biosignals with the same label can vary significantly among different subjects (i.e., intersubject variability) while biosignals with varied temporal patterns can be collected on the same subject (i.e., intrasubject variability). To address the challenges, we present the personalized meta-federated learning (PMFed) framework for personalized IoT-enabled health monitoring. Specifically, in the FL stage, a novel momentum-based model aggregating strategy is introduced to aggregate clients’ models based on domain similarity in the meta-FL paradigm to obtain a well-generalized global model while speeding up the convergence. In the model personalizing stage, an adaptive model personalization mechanism is devised to adaptively tailor the global model based on the subject-specific biosignal features while preserving the learned cross-subject representations. We develop an IoT-enabled computing framework to evaluate the effectiveness of PMFed over three real-world health monitoring tasks. Experimental results show that the PMFed excels at detection performances in terms of F1 and accuracy by up to 9.4% and 8.7%, and reduces training overhead and throughput by up to 56.3% and 63.4% when compared with the SOTA FL algorithms.

With the increment of data sets and models sizes, distributed deep learning has been proposed to accelerate training and improve the accuracy of DNN models. The parameter server framework is a popular collaborative architecture for data-parallel training, which works well for homogeneous environments by properly aggregating the computation/communication capabilities of different workers. However, in heterogeneous environments, the resources of different workers vary a lot. Some stragglers may seriously limit the whole speed, which impacts the overall training process. In this paper, we propose an adaptive multi-stage distributed deep learning training framework, named ASHL, for heterogeneous environments. First, a profiling scheme is proposed to capture the capabilities of each worker to reasonably plan the training and communication tasks on each worker, and lay the foundation for the formal training. Second, a hybrid-mode training scheme (i.e., coarse-grained and fined-grained training) is proposed to balance the model accuracy and training speed. The coarse-grained training scheme (named AHL) adopts an asynchronous communication strategy, which involves less frequent communications. Its main goal is to make the model quickly converge to a certain level. The fine-grained training stage (named SHL) uses a semi-asynchronous communication strategy and adopts a high communication frequency. Its main goal is to improve the model convergence effect. Finally, a compression-based communication scheme is proposed to further increase the communication efficiency of the training process. Our experimental results show that ASHL reduces the overall training time by more than 35% to converge to the same degree and has better generalization ability compared with state-of-the-art schemes like ADSP.