On the design and implementation of broadcast and global combine operations using the postal model

There are a number of models that were proposed in recent years for message passing parallel systems. Examples are the postal model and its generalization the LogP model. In the postal model a parameter λ is used to model the communication latency of the message-passing system. Each node during each round can send a fixed-size message and, simultaneously, receive a message of the same size. Furthermore, a message sent out during round r will incur a latency of hand will arrive at the receiving node at round r + λ - 1. Our goal in this paper is to bridge the gap between the theoretical modeling and the practical implementation. In particular, we investigate a number of practical issues related to the design and implementation of two collective communication operations, namely, the broadcast operation and the global combine operation. Those practical issues include, for example, 1) techniques for measurement of the value of λ on a given machine, 2) creating efficient broadcast algorithms that get the latency hand the number of nodes n as parameters and 3) creating efficient global combine algorithms for parallel machines with λ which is not an integer. We propose solutions that address those practical issues and present results of an experimental study of the new algorithms on the Intel Delta machine. Our main conclusion is that the postal model can help in performance prediction and tuning, for example, a properly tuned broadcast improves the known implementation by more than 20%

A Hybrid Approach to Privacy-Preserving Federated Learning

Federated learning facilitates the collaborative training of models without the sharing of raw data. However, recent attacks demonstrate that simply maintaining data locality during training processes does not provide sufficient privacy guarantees. Rather, we need a federated learning system capable of preventing inference over both the messages exchanged during training and the final trained model while ensuring the resulting model also has acceptable predictive accuracy. Existing federated learning approaches either use secure multiparty computation (SMC) which is vulnerable to inference or differential privacy which can lead to low accuracy given a large number of parties with relatively small amounts of data each. In this paper, we present an alternative approach that utilizes both differential privacy and SMC to balance these trade-offs. Combining differential privacy with secure multiparty computation enables us to reduce the growth of noise injection as the number of parties increases without sacrificing privacy while maintaining a pre-defined rate of trust. Our system is therefore a scalable approach that protects against inference threats and produces models with high accuracy. Additionally, our system can be used to train a variety of machine learning models, which we validate with experimental results on 3 different machine learning algorithms. Our experiments demonstrate that our approach out-performs state of the art solutions

Spartan Daily, October 3, 2000

Volume 115, Issue 23https://scholarworks.sjsu.edu/spartandaily/9590/thumbnail.jp

Spartan Daily, April 9, 2003

Volume 120, Issue 46https://scholarworks.sjsu.edu/spartandaily/9842/thumbnail.jp

Spartan Daily, September 3, 1993

Volume 101, Issue 6https://scholarworks.sjsu.edu/spartandaily/8434/thumbnail.jp

Spartan Daily, April 27, 1981

Volume 76, Issue 58https://scholarworks.sjsu.edu/spartandaily/6762/thumbnail.jp

The TREC2001 video track: information retrieval on digital video information

The development of techniques to support content-based access to archives of digital video information has recently started to receive much attention from the research community. During 2001, the annual TREC activity, which has been benchmarking the performance of information retrieval techniques on a range of media for 10 years, included a ”track“ or activity which allowed investigation into approaches to support searching through a video library. This paper is not intended to provide a comprehensive picture of the different approaches taken by the TREC2001 video track participants but instead we give an overview of the TREC video search task and a thumbnail sketch of the approaches taken by different groups. The reason for writing this paper is to highlight the message from the TREC video track that there are now a variety of approaches available for searching and browsing through digital video archives, that these approaches do work, are scalable to larger archives and can yield useful retrieval performance for users. This has important implications in making digital libraries of video information attainable

Spartan Daily, October 30, 1990

Volume 95, Issue 43https://scholarworks.sjsu.edu/spartandaily/8042/thumbnail.jp

Emerging Digital Frontiers for Service Innovation

This paper examines emerging digital frontiers for service innovation that a panel discussed at a workshop on this topic held at the 48th Annual Hawaii International Conference on System Sciences (HICSS). The speakers and participants agreed that that service systems are fundamental for service innovation and value creation. In this context, service systems are related to cognitive systems, smart service systems, and cyber-physical systems and depend on the interconnectedness among system components. The speakers and participants regarded humans as the central entity in all service systems. In addition, data, they saw personal data in particular as key to service systems. They also identified several challenges in the areas of cognitive systems, smart service systems, cyber-physical systems, and human-centered service systems. We hope this workshop report helps in some small way to cultivate the emerging service science discipline and to nurture fruitful discussions on service innovation