Achieving reliability and fairness in online task computing environments

Mención Internacional en el título de doctorWe consider online task computing environments such as volunteer computing platforms running on BOINC (e.g., SETI@home) and crowdsourcing platforms such as Amazon Mechanical Turk. We model the computations as an Internet-based task computing system under the masterworker paradigm. A master entity sends tasks across the Internet, to worker entities willing to perform a computational task. Workers execute the tasks, and report back the results, completing the computational round. Unfortunately, workers are untrustworthy and might report an incorrect result. Thus, the first research question we answer in this work is how to design a reliable masterworker task computing system. We capture the workers’ behavior through two realistic models: (1) the “error probability model” which assumes the presence of altruistic workers willing to provide correct results and the presence of troll workers aiming at providing random incorrect results. Both types of workers suffer from an error probability altering their intended response. (2) The “rationality model” which assumes the presence of altruistic workers, always reporting a correct result, the presence of malicious workers always reporting an incorrect result, and the presence of rational workers following a strategy that will maximize their utility (benefit). The rational workers can choose among two strategies: either be honest and report a correct result, or cheat and report an incorrect result. Our two modeling assumptions on the workers’ behavior are supported by an experimental evaluation we have performed on Amazon Mechanical Turk. Given the error probability model, we evaluate two reliability techniques: (1) “voting” and (2) “auditing” in terms of task assignments required and time invested for computing correctly a set of tasks with high probability. Considering the rationality model, we take an evolutionary game theoretic approach and we design mechanisms that eventually achieve a reliable computational platform where the master receives the correct task result with probability one and with minimal auditing cost. The designed mechanisms provide incentives to the rational workers, reinforcing their strategy to a correct behavior, while they are complemented by four reputation schemes that cope with malice. Finally, we also design a mechanism that deals with unresponsive workers by keeping a reputation related to the workers’ response rate. The designed mechanism selects the most reliable and active workers in each computational round. Simulations, among other, depict the trade-off between the master’s cost and the time the system needs to reach a state where the master always receives the correct task result. The second research question we answer in this work concerns the fair and efficient distribution of workers among the masters over multiple computational rounds. Masters with similar tasks are competing for the same set of workers at each computational round. Workers must be assigned to the masters in a fair manner; when the master values a worker’s contribution the most. We consider that a master might have a strategic behavior, declaring a dishonest valuation on a worker in each round, in an attempt to increase its benefit. This strategic behavior from the side of the masters might lead to unfair and inefficient assignments of workers. Applying renown auction mechanisms to solve the problem at hand can be infeasible since monetary payments are required on the side of the masters. Hence, we present an alternative mechanism for fair and efficient distribution of the workers in the presence of strategic masters, without the use of monetary incentives. We show analytically that our designed mechanism guarantees fairness, is socially efficient, and is truthful. Simulations favourably compare our designed mechanism with two benchmark auction mechanisms.This work has been supported by IMDEA Networks Institute and the Spanish Ministry of Education grant FPU2013-03792.Programa Oficial de Doctorado en Ingeniería MatemáticaPresidente: Alberto Tarable.- Secretario: José Antonio Cuesta Ruiz.- Vocal: Juan Julián Merelo Guervó

Multi-round Master-Worker Computing: a Repeated Game Approach

We consider a computing system where a master processor assigns tasks for execution to worker processors through the Internet. We model the workers decision of whether to comply (compute the task) or not (return a bogus result to save the computation cost) as a mixed extension of a strategic game among workers. That is, we assume that workers are rational in a game-theoretic sense, and that they randomize their strategic choice. Workers are assigned multiple tasks in subsequent rounds. We model the system as an infinitely repeated game of the mixed extension of the strategic game. In each round, the master decides stochastically whether to accept the answer of the majority or verify the answers received, at some cost. Incentives and/or penalties are applied to workers accordingly. Under the above framework, we study the conditions in which the master can reliably obtain tasks results, exploiting that the repeated games model captures the effect of long-term interaction. That is, workers take into account that their behavior in one computation will have an effect on the behavior of other workers in the future. Indeed, should a worker be found to deviate from some agreed strategic choice, the remaining workers would change their own strategy to penalize the deviator. Hence, being rational, workers do not deviate. We identify analytically the parameter conditions to induce a desired worker behavior, and we evaluate experi- mentally the mechanisms derived from such conditions. We also compare the performance of our mechanisms with a previously known multi-round mechanism based on reinforcement learning.Comment: 21 pages, 3 figure

Applying the dynamics of evolution to achieve reliability in master-worker computing

We consider Internet-based master-worker task computations, such as SETI@home, where a master process sends tasks, across the Internet, to worker processes; workers execute and report back some result. However, these workers are not trustworthy, and it might be at their best interest to report incorrect results. In such master-worker computations, the behavior and the best interest of the workers might change over time. We model such computations using evolutionary dynamics, and we study the conditions under which the master can reliably obtain task results. In particular, we develop and analyze an algorithmic mechanism based on reinforcement learning to provide workers with the necessary incentives to eventually become truthful. Our analysis identifies the conditions under which truthful behavior can be ensured and bounds the expected convergence time to that behavior. The analysis is complemented with illustrative simulations.This work is supported by the Cyprus Research Promotion Foundation grant TΠE/ΠΛHPO/0609(BE)/05, the National Science Foundation (CCF-0937829, CCF-1114930), Comunidad de Madrid grants S2009TIC-1692 and MODELICO-CM, Spanish PRODIEVO and RESINEE grants and MICINN grant EC2011-29688-C02-01, and National Natural Science Foundation of China grant 61020106002.Publicad

Pando: Personal Volunteer Computing in Browsers

The large penetration and continued growth in ownership of personal electronic devices represents a freely available and largely untapped source of computing power. To leverage those, we present Pando, a new volunteer computing tool based on a declarative concurrent programming model and implemented using JavaScript, WebRTC, and WebSockets. This tool enables a dynamically varying number of failure-prone personal devices contributed by volunteers to parallelize the application of a function on a stream of values, by using the devices' browsers. We show that Pando can provide throughput improvements compared to a single personal device, on a variety of compute-bound applications including animation rendering and image processing. We also show the flexibility of our approach by deploying Pando on personal devices connected over a local network, on Grid5000, a French-wide computing grid in a virtual private network, and seven PlanetLab nodes distributed in a wide area network over Europe.Comment: 14 pages, 12 figures, 2 table

Trustworthy Federated Learning: A Survey

Federated Learning (FL) has emerged as a significant advancement in the field of Artificial Intelligence (AI), enabling collaborative model training across distributed devices while maintaining data privacy. As the importance of FL increases, addressing trustworthiness issues in its various aspects becomes crucial. In this survey, we provide an extensive overview of the current state of Trustworthy FL, exploring existing solutions and well-defined pillars relevant to Trustworthy . Despite the growth in literature on trustworthy centralized Machine Learning (ML)/Deep Learning (DL), further efforts are necessary to identify trustworthiness pillars and evaluation metrics specific to FL models, as well as to develop solutions for computing trustworthiness levels. We propose a taxonomy that encompasses three main pillars: Interpretability, Fairness, and Security & Privacy. Each pillar represents a dimension of trust, further broken down into different notions. Our survey covers trustworthiness challenges at every level in FL settings. We present a comprehensive architecture of Trustworthy FL, addressing the fundamental principles underlying the concept, and offer an in-depth analysis of trust assessment mechanisms. In conclusion, we identify key research challenges related to every aspect of Trustworthy FL and suggest future research directions. This comprehensive survey serves as a valuable resource for researchers and practitioners working on the development and implementation of Trustworthy FL systems, contributing to a more secure and reliable AI landscape.Comment: 45 Pages, 8 Figures, 9 Table