The Buffered \pi-Calculus: A Model for Concurrent Languages

Message-passing based concurrent languages are widely used in developing large distributed and coordination systems. This paper presents the buffered $\pi$-calculus --- a variant of the $\pi$-calculus where channel names are classified into buffered and unbuffered: communication along buffered channels is asynchronous, and remains synchronous along unbuffered channels. We show that the buffered $\pi$-calculus can be fully simulated in the polyadic $\pi$-calculus with respect to strong bisimulation. In contrast to the $\pi$-calculus which is hard to use in practice, the new language enables easy and clear modeling of practical concurrent languages. We encode two real-world concurrent languages in the buffered $\pi$-calculus: the (core) Go language and the (Core) Erlang. Both encodings are fully abstract with respect to weak bisimulations

Towards Fairness-Aware Federated Learning

Recent advances in Federated Learning (FL) have brought large-scale collaborative machine learning opportunities for massively distributed clients with performance and data privacy guarantees. However, most current works focus on the interest of the central controller in FL,and overlook the interests of the FL clients. This may result in unfair treatment of clients which discourages them from actively participating in the learning process and damages the sustainability of the FL ecosystem. Therefore, the topic of ensuring fairness in FL is attracting a great deal of research interest. In recent years, diverse Fairness-Aware FL (FAFL) approaches have been proposed in an effort to achieve fairness in FL from different perspectives. However, there is no comprehensive survey which helps readers gain insight into this interdisciplinary field. This paper aims to provide such a survey. By examining the fundamental and simplifying assumptions, as well as the notions of fairness adopted by existing literature in this field, we propose a taxonomy of FAFL approaches covering major steps in FL, including client selection, optimization, contribution evaluation and incentive distribution. In addition, we discuss the main metrics for experimentally evaluating the performance of FAFL approaches, and suggest promising future research directions towards fairness-aware federated learning.Comment: 16 pages, 4 figure

Income Tax and Salesforce Performance: A Micro Perspective

How does the change in income tax affect sales performance? Our paper explores the link between economic policy and salesforce management at the transactional level, using data from a large fashion retailer in China. The analysis shows that the implementation of a nationwide personal income tax cut in October 2018 improves sales performance more among those salespersons who benefited from the policy, compare to others. The performance gain is observed after the tax cut and persisted in future months, and is particularly significant in low-income regions. We also find that the performance gain is largely due to an increase in sales of pricier items, rather than more reliance on discounts. Finally, the research shows that the net effect of the tax cut is an increase in the government’s revenue due to the firm’s higher sales and corresponding increase in corporate tax paid

Mechanisms for Dynamic Setting with Restricted Allocations

Dynamic mechanism design is an important area of multiagent systems, and commonly used in resource allocation where the resources are time related or the agents exist dynamically. We focus on a multiagent model within which the agents stay, and the resources arrive and depart. The resources are interpreted as work or jobs and are called tasks. The allocation outcome space has a special restriction that every agent can only work on one resource at a time, because every agent has a finite computational capability in reality. We propose a dynamic mechanism and analyze its incentive properties; we show that the mechanism is incentive compatible. Empirically, our dynamic mechanism performs well and is able to achieve high economic efficiency, even outperforming standard approaches if the agents are concerned about future tasks. We also introduce a static mechanism under the setting of a restricted outcome space; it is proved that the static mechanism is incentive compatible, and its computational complexity is much less than that of the standard VCG mechanism