Huijing Zhang

Di Yu, Xin Du, Linshan Jiang et al.

The energy efficiency of deep spiking neural networks (SNNs) aligns with the constraints of resource-limited edge devices, positioning SNNs as a promising foundation for intelligent applications leveraging the extensive data collected by these devices. To address data privacy concerns when deploying SNNs on edge devices, federated learning (FL) facilitates collaborative model training by leveraging data distributed across edge devices without transmitting local data to a central server. However, existing FL approaches struggle with label-skewed data across devices, which leads to drift in local SNN models and degrades the performance of the global SNN model. In this paper, we propose a novel framework called FedLEC, which incorporates intra-client label weight calibration to balance the learning intensity across local labels and inter-client knowledge distillation to mitigate local SNN model bias caused by label absence. Extensive experiments with three different structured SNNs across five datasets (i.e., three non-neuromorphic and two neuromorphic datasets) demonstrate the efficiency of FedLEC. Compared to eight state-of-the-art FL algorithms, FedLEC achieves an average accuracy improvement of approximately 11.59% for the global SNN model under various label skew distribution settings.

Huijing Zhang, Muyang Cao, Linshan Jiang et al.

Continuous learning of novel classes is crucial for edge devices to preserve data privacy and maintain reliable performance in dynamic environments. However, the scenario becomes particularly challenging when data samples are insufficient, requiring on-device few-shot class-incremental learning (FSCIL) to maintain consistent model performance. Although existing work has explored parameter-efficient FSCIL frameworks based on artificial neural networks (ANNs), their deployment is still fundamentally constrained by limited device resources. Inspired by neural mechanisms, Spiking neural networks (SNNs) process spatiotemporal information efficiently, offering lower energy consumption, greater biological plausibility, and compatibility with neuromorphic hardware than ANNs. In this work, we present an SNN-based method for On-Device FSCIL, i.e., Sparsity-Aware and Fast Adaptive SNN (SAFA-SNN). We first propose sparsity-conditioned neuronal dynamics, in which most neurons remain stable while a subset stays active, thereby mitigating catastrophic forgetting. To further cope with spike non-differentiability in gradient estimation, we employ zeroth-order optimization. Moreover, during incremental learning sessions, we enhance the discriminability of new classes through subspace projection, which alleviates overfitting to novel classes. Extensive experiments conducted on two standard benchmark datasets (CIFAR100 and Mini-ImageNet) and three neuromorphic datasets (CIFAR-10-DVS, DVS128gesture, and N-Caltech101) demonstrate that SAFA-SNN outperforms baseline methods, specifically achieving at least 4.01% improvement at the last incremental session on Mini-ImageNet and 20% lower energy cost over baseline methods with practical implementation.

Huijing Zhang, David Choffnes

Mobile devices that connect to the Internet via cellular networks are rapidly becoming the primary medium for accessing Web content. Cellular service providers (CSPs) commonly deploy Web proxies and other middleboxes for security, performance optimization and traffic engineering reasons. However, the prevalence and policies of these Web proxies are generally opaque to users and difficult to measure without privileged access to devices and servers. In this paper, we present a methodology to detect the presence of Web proxies without requiring access to low-level packet traces on a device, nor access to servers being contacted. We demonstrate the viability of this technique using controlled experiments, and present the results of running our approach on several production networks and popular Web sites. Next, we characterize the behaviors of these Web proxies, including caching, redirecting, and content rewriting. Our analysis can identify how Web proxies impact network performance, and inform policies for future deployments. Last, we release an Android app called Proxy Detector on the Google Play Store, allowing average users with unprivileged (non-rooted) devices to understand Web proxy deployments and contribute to our IRB-approved study. We report on results of using this app on 11 popular carriers from the US, Canada, Austria, and China.