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