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Addressing Algorithmic Disparity and Performance Inconsistency in Federated Learning
Sen Cui · Weishen Pan · Jian Liang · Changshui Zhang · Fei Wang

Thu Dec 09 12:30 AM -- 02:00 AM (PST) @

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.

Author Information

Sen Cui (Tsinghua University)
Weishen Pan (Tsinghua University, Tsinghua University)
Jian Liang (Alibaba Group)

Jian Liang received his Ph.D. degree from Tsinghua University, Beijing, China, in 2018. During 2018 and 2020 he was a senior researcher in the Wireless Security Products Department of the Cloud and Smart Industries Group at Tencent, Beijing. In 2020 he joined the AI for international Department, New Retail Intelligence Engine, Alibaba Group as a senior algorithm engineer. His paper received the Best Short Paper Award in 2016 IEEE International Conference on Healthcare Informatics (ICHI).

Changshui Zhang (Tsinghua University)
Fei Wang (Cornell University)

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