Fairness in Federated Learning via Core-Stability

Federated learning provides an effective paradigm to jointly optimize a model benefited from rich distributed data while protecting data privacy. Nonetheless, the heterogeneity nature of distributed data makes it challenging to define and ensure fairness among local agents. For instance, it is intuitively "unfair" for agents with data of high quality to sacrifice their performance due to other agents with low quality data. Currently popular egalitarian and weighted equity-based fairness measures suffer from the aforementioned pitfall. In this work, we aim to formally represent this problem and address these fairness issues using concepts from co-operative game theory and social choice theory. We model the task of learning a shared predictor in the federated setting as a fair public decision making problem, and then define the notion of core-stable fairness: Given $N$ agents, there is no subset of agents $S$ that can benefit significantly by forming a coalition among themselves based on their utilities $U_N$ and $U_S$ (i.e., $\frac{|S|}{N} U_S \geq U_N$). Core-stable predictors are robust to low quality local data from some agents, and additionally they satisfy Proportionality and Pareto-optimality, two well sought-after fairness and efficiency notions within social choice. We then propose an efficient federated learning protocol CoreFed to optimize a core stable predictor. CoreFed determines a core-stable predictor when the loss functions of the agents are convex. CoreFed also determines approximate core-stable predictors when the loss functions are not convex, like smooth neural networks. We further show the existence of core-stable predictors in more general settings using Kakutani's fixed point theorem. Finally, we empirically validate our analysis on two real-world datasets, and we show that CoreFed achieves higher core-stability fairness than FedAvg while having similar accuracy.Comment: NeurIPS 2022; code: https://openreview.net/attachment?id=lKULHf7oFDo&name=supplementary_materia

Construir el diálogo científico en la Matemática: la búsqueda del equilibrio entre símbolos y palabras en artículos de investigación sobre Teoría de Juegos

Maestría en Inglés con Orientación en Lingüística AplicadaMost scientific communication is conducted in English, which may be a difficult task and a source of obstacles for researchers whose primary language is not English (Bitchenera & Basturkmen, 2006; Borlogan, 2009; Duff, 2010; Matsuda & Matsuda, 2010). As a matter of concern for language scholars, this situation requires at least two actions: (1) the development of research focused on the problems faced by researchers when writing in a foreign language, and (2) the design and implementation of pedagogical and didactic programmes or services aimed at providing researchers with the tools to enhance their linguistic and rhetorical skills. In both cases, the ultimate objective of these lines of action is to help researchers integrate into and interact with their knowledge communities in an independent, active and successful way. Considering those needs and the emerging interest in English as a lingua franca or as an international language, many scholars have devoted to studying the features of writing and language use across the world and across disciplines (Hyland, 2004; Matsuda & Matsuda, 2010; Mercado, 2010). However, few have explored the case of Mathematics (Lemke, 2002; Morgan, 2008; O’Halloran, 2005; Schleppegrell, 2007), and even fewer have investigated the discourse of scientific research articles (SRA) in this discipline (Graves & Moghadassi, 2013, 2014). In view of this situation, investigation of the discourse of science in the field of Mathematics (Game Theory - GT) as used in the Institute of Applied Mathematics (IMASL), at the National University of San Luis (UNSL), becomes both an answer to local researchers’ needs and an attempt to contribute to current research in writing, evaluative discourse and use of English as an international language for the communication of science. Thus, the main objective of this work is to conduct a comparative description between unpublished GT SRAs written in English by IMASL researchers and published GT SRAs written in English by international authors, in terms of linguistic features used to build authorship and authorial stance. The exploration of the genre is made from the perspective of the system of Appraisal (Hood, 2010; Martin & White, 2005; White, 2000), with the aid of Corpus Linguistics (CL) tools (Cheng, 2012; Meyer, 2002; Tognini-Bonelli, 2001). The results of this research are expected to be useful for the enhancement of knowledge of language professionals devoted to the teaching of writing as well as translation, proofreading, editing and reviewing services. A further goal is to lay the foundations for the production of didactic material which can potentially be incorporated into writing courses or professional writing, translation, reviewing and proofreading training programmes.Fil: Lucero Arrua, Graciela Beatriz. Universidad Nacional de Córdoba. Facultad de Lenguas; Argentina

Proportionality and Fairness in Voting and Ranking Systems

Fairness through proportionality has received significant attention in recent social choice research, leading to the development of advanced tools, methods, and algorithms aimed at ensuring fairness in democratic institutions. Citizen-focused democratic processes where participants deliberate on alternatives and then vote to make the final decision are increasingly popular today. While the computational social choice literature has extensively investigated voting rules, there is limited work that explicitly looks at the interplay of the deliberative process and voting. In this thesis, we build a deliberation model using established models from the opinion-dynamics literature and study the effect of different deliberation mechanisms on voting outcomes achieved when using well-studied voting rules. Our results show that deliberation generally improves welfare and representation guarantees, but the results are sensitive to how the deliberation process is organized. We also show, experimentally, that simple voting rules, such as approval voting, perform as well as more sophisticated rules such as proportional approval voting or method of equal shares if deliberation is properly supported. This has ramifications on the practical use of such voting rules in citizen-focused democratic processes. Intricately designed proportional voting rules offer robust theoretical and axiomatic fairness guarantees that can prove valuable in similar scenarios beyond the realm of elections. In the second part, we capitalize on these properties and introduce innovative fair-ranking algorithms based on proportional voting methods. Specifically, we define the general task of fair ranking, which involves generating a list of items that is fairly ordered with respect to a given query, as a voting problem. Our findings reveal that proportional voting rules deliver exceptional performance, frequently matching or surpassing the performance of existing benchmarks in terms of aggregate fairness and relevance metrics. These discoveries present exciting avenues for further research and applications, endorsing the widespread adoption of proportional voting rules in domains where fairness is a priority

Contracting in the Presence of Uncertainty

This thesis concerns the enforcement of contracts in the presence of uncertainty. Sometimes uncertainty is exogenously given and the agents cannot influence its existence. Frequently, however, there is strategic uncertainty created by the behavior of other agents. Both kinds of uncertainty have important impacts on contracting. Chapter 1 shows that insurers use uncertainty about auditing strategies to fight insurance fraud. For this purpose, we study a costly state verification model with ambiguity. The insurers abstain from commitment to an auditing strategy, even if commitment is possible without incurring any costs. This contrasts with conventional wisdom, which claims that it is optimal to commit, as the credible announcement of thoroughly auditing claim reports might act as a powerful deterrent to insurance fraud. Yet, empirically it is very unusual for insurers to try to overcome the credibility issue. We prove that it can be optimal for the insurers to maintain the ambiguity and forgo commitment. Thus, strategic ambiguity, i.e., the strategic choice to withhold information about auditing costs and strategies, is an equilibrium outcome. The second chapter considers legal uncertainty in competition law. I show that legal uncertainty can be welfare-enhancing, if the uncertainty is not too large. As an example, consider Article 101 (TFEU) prohibiting vertical restraints with a block exemption excluding companies with market shares below 30%. There are guidelines available how the relevant market shares are to be calculated. Nevertheless, it is extremely difficult to predict correctly the market share that the competition authorities will determine. In addition, there is uncertainty about the size of the fine that firms have to pay in case of a conviction. This exemplifies legal uncertainty as scrutinized in the chapter. The third chapter analyzes a principal-agent model, in which the performance measure of the principal is non-verifiable and unobservable by the agent. Instead, the principal has the possibility to communicate with the agent. The communication occurs at the very end of the interaction and there is no repeated interaction. Nevertheless, it is crucial for the agent’s motivation that the principal gives feedback and justifies her evaluation, in particular, in case of bad outcomes. In addition, it is optimal to pool evaluations and to compress wages at the top. These results fit well with empirical observations, like the leniency bias and the centrality bias. Hence, this pattern of evaluations can be understood as a feature of the optimal contract instead of biased behavior. Corresponding to the distinction between first-order and second-order risk aversion, the fourth chapter defines first-order and second-order ambiguity aversion. With second-order ambiguity aversion, for every ambiguity-averse agent there is an ambiguity-neutral agent so that the set of all improvement directions at an unambiguous endowment is the same for both agents. With first-order ambiguity aversion, in contrast, the set of improvement directions is a strict subset of the improvement directions of an ambiguity-neutral agent. Chapter 4 provides three equivalent definitions for the distinction. For this purpose, I introduce a general ambiguity premium and a notion of reference beliefs of an ambiguity-averse agent. This distinction has direct implications for settings in finance, insurance, and contracting. In particular, I consider the validity of an adapted version of Holmström’s informativeness principle under ambiguity aversion