Confidant: Customizing Transformer-based LLMs via Collaborative Edge Training

Transformer-based large language models (LLMs) have demonstrated impressive capabilities in a variety of natural language processing (NLP) tasks. Nonetheless, it is challenging to deploy and fine-tune LLMs on mobile edge devices with limited computing, memory, and energy budgets. In this paper, we propose Confidant, a multi-backend collaborative training framework for customizing state-of-the-art LLMs on commodity mobile devices like smartphones. Confidant partitions an LLM into several sub-models so that each fits into a mobile device's memory. A pipeline parallel training mechanism is further developed to ensure fast and efficient distributed training. In addition, we propose a novel backend scheduler to allocate different attention heads to heterogeneous compute hardware, including mobile CPU and GPUs, to maximize the compute resource utilization on each edge device. Our preliminary experimental results show that Confidant achieves at most 45.3% memory reduction and 8.03x inference speedup in practical settings.Comment: 6 pages, 7 figures; Submitted to HotMobile 202

AccEPT: An Acceleration Scheme for Speeding Up Edge Pipeline-parallel Training

It is usually infeasible to fit and train an entire large deep neural network (DNN) model using a single edge device due to the limited resources. To facilitate intelligent applications across edge devices, researchers have proposed partitioning a large model into several sub-models, and deploying each of them to a different edge device to collaboratively train a DNN model. However, the communication overhead caused by the large amount of data transmitted from one device to another during training, as well as the sub-optimal partition point due to the inaccurate latency prediction of computation at each edge device can significantly slow down training. In this paper, we propose AccEPT, an acceleration scheme for accelerating the edge collaborative pipeline-parallel training. In particular, we propose a light-weight adaptive latency predictor to accurately estimate the computation latency of each layer at different devices, which also adapts to unseen devices through continuous learning. Therefore, the proposed latency predictor leads to better model partitioning which balances the computation loads across participating devices. Moreover, we propose a bit-level computation-efficient data compression scheme to compress the data to be transmitted between devices during training. Our numerical results demonstrate that our proposed acceleration approach is able to significantly speed up edge pipeline parallel training up to 3 times faster in the considered experimental settings

Cleavage of a pathogen apoplastic protein by plant subtilases activates host immunity

The plant apoplast is a harsh environment in which hydrolytic enzymes, especially proteases, accumulate during pathogen infection. However, the defense functions of most apoplastic proteases remain largely elusive. We show that a newly identified small cysteine rich secreted protein PC2 from the potato late blight pathogen Phytophthora infestans induces immunity in Solanum plants only after cleavage by plant apoplastic subtilisin‐like proteases, such as tomato P69B. A minimal 61‐amino‐acid core peptide carrying two key cysteines, conserved widely in most oomycete species, is sufficient for PC2‐induced cell death. Furthermore, we showed that Kazal‐like protease inhibitors, such as EPI1 produced by P. infestans prevent PC2 cleavage and dampen PC2 elicited host immunity. This study reveals that cleavage of pathogen proteins to release immunogenic peptides is an important function of plant apoplastic proteases

Dorsal Visual Pathway Changes in Patients with Comitant Extropia

BACKGROUND: Strabismus is a disorder in which the eyes are misaligned. Persistent strabismus can lead to stereopsis impairment. The effect of strabismus on human brain is not unclear. The present study is to investigate whether the brain white structures of comitant exotropia patients are impaired using combined T1-weighted imaging and diffusion tensor imaging (DTI). PRINCIPAL FINDINGS: Thirteen patients with comitant strabismus and twelve controls underwent magnetic resonance imaging (MRI) with acquisition of T1-weighted and diffusion tensor images. T1-weighted images were used to analyze the change in volume of white matter using optimized voxel-based morphology (VBM) and diffusion tensor images were used to detect the change in white matter fibers using voxel-based analysis of DTI in comitant extropia patients. VBM analysis showed that in adult strabismus, white matter volumes were smaller in the right middle occipital gyrus, right occipital lobe/cuneus, right supramarginal gyrus, right cingulate gyrus, right frontal lobe/sub-gyral, right inferior temporal gyrus, left parahippocampa gyrus, left cingulate gyrus, left occipital lobe/cuneus, left middle frontal gyrus, left inferior parietal lobule, and left postcentral gyrus, while no brain region with greater white matter volume was found. Voxel-based analysis of DTI showed lower fractional anisotropy (FA) values in the right middle occipital gyrus and right supramarginal gyrus in strabismus patients, while brain region with increased FA value was found in the right inferior frontal gyrus. CONCLUSION: By combining VBM and voxel-based analysis of DTI results, the study suggests that the dorsal visual pathway was abnormal or impaired in patients with comitant exotropia

Sensory-Data-Enhanced Authentication for RFID-based Access Control Systems

Abstract—Access card authentication is critical and essential for many modern access control systems, which have been widely deployed in various government, commercial and residential environments. However, due to the static identification information exchange among the access cards and access control clients, it is very challenging to fight against access control system breaches due to reasons such as loss, stolen or unauthorized duplications of the access cards. Although advanced biometric authentication methods such as fingerprint and iris identification can further identify the user who is requesting authorization, they incur high system costs and access privileges can not be transferred among trusted users. In this work, we introduce a sensory-dataenhanced authentication for access control systems. By combining sensory-data obtained from onboard sensors on the access cards as well as the original encoded identification information, we are able to effectively tackle the problems such as access card loss and stolen. Our solution is backward-compatible with existing access control systems and significantly increases the key spaces for authentication. We theoretically demonstrate the potential key space increases with simple sensor data and empirically demonstrate simple rotations can increase key space by more than 30, 000 times with an authentication accuracy of 95%. We performed extensive simulations under various environment settings and implemented our design on WISP to experimentally verify the system performance. I

Multi-View Domain Adaptive Object Detection on Camera Networks

In this paper, we study a new domain adaptation setting on camera networks, namely Multi-View Domain Adaptive Object Detection (MVDA-OD), in which labeled source data is unavailable in the target adaptation process and target data is captured from multiple overlapping cameras. In such a challenging context, existing methods including adversarial training and self-training fall short due to multi-domain data shift and the lack of source data. To tackle this problem, we propose a novel training framework consisting of two stages. First, we pre-train the backbone using self-supervised learning, in which a multi-view association is developed to construct an effective pretext task. Second, we fine-tune the detection head using robust self-training, where a tracking-based single-view augmentation is introduced to achieve weak-hard consistency learning. By doing so, an object detection model can take advantage of informative samples generated by multi-view association and single-view augmentation to learn discriminative backbones as well as robust detection classifiers. Experiments on two real-world multi-camera datasets demonstrate significant advantages of our approach over the state-of-the-art domain adaptive object detection methods