56 research outputs found
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 agents, there is no subset of
agents that can benefit significantly by forming a coalition among
themselves based on their utilities and (i.e., ). 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
Fair Knapsack
We study the following multiagent variant of the knapsack problem. We are
given a set of items, a set of voters, and a value of the budget; each item is
endowed with a cost and each voter assigns to each item a certain value. The
goal is to select a subset of items with the total cost not exceeding the
budget, in a way that is consistent with the voters' preferences. Since the
preferences of the voters over the items can vary significantly, we need a way
of aggregating these preferences, in order to select the socially best valid
knapsack. We study three approaches to aggregating voters' preferences, which
are motivated by the literature on multiwinner elections and fair allocation.
This way we introduce the concepts of individually best, diverse, and fair
knapsack. We study the computational complexity (including parameterized
complexity, and complexity under restricted domains) of the aforementioned
multiagent variants of knapsack.Comment: Extended abstract will appear in Proc. of 33rd AAAI 201
Sub-committee Approval Voting and Generalised Justified Representation Axioms
Social choice is replete with various settings including single-winner
voting, multi-winner voting, probabilistic voting, multiple referenda, and
public decision making. We study a general model of social choice called
Sub-Committee Voting (SCV) that simultaneously generalizes these settings. We
then focus on sub-committee voting with approvals and propose extensions of the
justified representation axioms that have been considered for proportional
representation in approval-based committee voting. We study the properties and
relations of these axioms. For each of the axioms, we analyse whether a
representative committee exists and also examine the complexity of computing
and verifying such a committee
Fair and Efficient Allocations under Subadditive Valuations
We study the problem of allocating a set of indivisible goods among agents
with subadditive valuations in a fair and efficient manner. Envy-Freeness up to
any good (EFX) is the most compelling notion of fairness in the context of
indivisible goods. Although the existence of EFX is not known beyond the simple
case of two agents with subadditive valuations, some good approximations of EFX
are known to exist, namely -EFX allocation and EFX allocations
with bounded charity.
Nash welfare (the geometric mean of agents' valuations) is one of the most
commonly used measures of efficiency. In case of additive valuations, an
allocation that maximizes Nash welfare also satisfies fairness properties like
Envy-Free up to one good (EF1). Although there is substantial work on
approximating Nash welfare when agents have additive valuations, very little is
known when agents have subadditive valuations. In this paper, we design a
polynomial-time algorithm that outputs an allocation that satisfies either of
the two approximations of EFX as well as achieves an
approximation to the Nash welfare. Our result also improves the current
best-known approximation of and to
Nash welfare when agents have submodular and subadditive valuations,
respectively.
Furthermore, our technique also gives an approximation to a
family of welfare measures, -mean of valuations for ,
thereby also matching asymptotically the current best known approximation ratio
for special cases like while also retaining the fairness
properties
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