Fair and Consistent Federated Learning
- URL: http://arxiv.org/abs/2108.08435v1
- Date: Thu, 19 Aug 2021 01:56:08 GMT
- Title: Fair and Consistent Federated Learning
- Authors: Sen Cui, Weishen Pan, Jian Liang, Changshui Zhang, Fei Wang
- Abstract summary: Federated learning (FL) has gain growing interests for its capability of learning from distributed data sources collectively.
We propose an FL framework to jointly consider performance consistency and algorithmic fairness across different local clients.
- Score: 48.19977689926562
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: Federated learning (FL) has gain growing interests for its capability of
learning from distributed data sources collectively without the need of
accessing the raw data samples across different sources. So far FL research has
mostly focused on improving the performance, how the algorithmic disparity will
be impacted for the model learned from FL and the impact of algorithmic
disparity on the utility inconsistency are largely unexplored. In this paper,
we propose an FL framework to jointly consider performance consistency and
algorithmic fairness across different local clients (data sources). We derive
our framework from a constrained multi-objective optimization perspective, in
which we learn a model satisfying fairness constraints on all clients with
consistent performance. Specifically, we treat the algorithm prediction loss at
each local client as an objective and maximize the worst-performing client with
fairness constraints through optimizing a surrogate maximum function with all
objectives involved. A gradient-based procedure is employed to achieve the
Pareto optimality of this optimization problem. Theoretical analysis is
provided to prove that our method can converge to a Pareto solution that
achieves the min-max performance with fairness constraints on all clients.
Comprehensive experiments on synthetic and real-world datasets demonstrate the
superiority that our approach over baselines and its effectiveness in achieving
both fairness and consistency across all local clients.
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