skip to main content


Search for: All records

Creators/Authors contains: "Wang, Zhan"

Note: When clicking on a Digital Object Identifier (DOI) number, you will be taken to an external site maintained by the publisher. Some full text articles may not yet be available without a charge during the embargo (administrative interval).
What is a DOI Number?

Some links on this page may take you to non-federal websites. Their policies may differ from this site.

  1. Source code, experiments and database of SIMPLE-G for researching the impact of human heat stress on US agriculture. 
    more » « less
  2. Free, publicly-accessible full text available April 10, 2025
  3. With the ever-increasing size of training models and datasets, network communication has emerged as a major bottleneck in distributed deep learning training. To address this challenge, we propose an optical distributed deep learning (ODDL) architecture. ODDL utilizes a fast yet scalable all-optical network architecture to accelerate distributed training. One of the key features of the architecture is its flow-based transmit scheduling with fast reconfiguration. This allows ODDL to allocate dedicated optical paths for each traffic stream dynamically, resulting in low network latency and high network utilization. Additionally, ODDL provides physically isolated and tailored network resources for training tasks by reconfiguring the optical switch using LCoS-WSS technology. The ODDL topology also uses tunable transceivers to adapt to time-varying traffic patterns. To achieve accurate and fine-grained scheduling of optical circuits, we propose an efficient distributed control scheme that incurs minimal delay overhead. Our evaluation on real-world traces showcases ODDL’s remarkable performance. When implemented with 1024 nodes and 100 Gbps bandwidth, ODDL accelerates VGG19 training by 1.6× and 1.7× compared to conventional fat-tree electrical networks and photonic SiP-Ring architectures, respectively. We further build a four-node testbed, and our experiments show that ODDL can achieve comparable training time compared to that of anidealelectrical switching network.

     
    more » « less
  4. We introduce MobiCeal, the first practical Plausibly Deniable Encryption (PDE) system for mobile devices that can defend against strong coercive multi-snapshot adversaries, who may examine the storage medium of a user’s mobile device at different points of time and force the user to decrypt data. MobiCeal relies on “dummy write” to obfuscate the differences between multiple snapshots of storage medium due to data encryption. By combining a tweaked thin provisioning with block- level encryption, MobiCeal supports a broad deployment of any block-based file systems on mobile devices. More importantly, MobiCeal is secure against side channel attacks which pose a serious threat to existing PDE schemes. A new fast switching mechanism is also introduced in MobiCeal to help users switch from the public mode to the hidden mode within 10 seconds. It is shown that the performance of MobiCeal is significantly better than prior PDE systems against multi-snapshot adversaries. 
    more » « less