Federated learning (FL) is a promising distributed framework for
collaborative artificial intelligence model training while protecting user
privacy. A bootstrapping component that has attracted significant research
attention is the design of incentive mechanism to stimulate user collaboration
in FL. The majority of works adopt a broker-centric approach to help the
central operator to attract participants and further obtain a well-trained
model. Few works consider forging participant-centric collaboration among
participants to pursue an FL model for their common interests, which induces
dramatic differences in incentive mechanism design from the broker-centric FL.
To coordinate the selfish and heterogeneous participants, we propose a novel
analytic framework for incentivizing effective and efficient collaborations for
participant-centric FL. Specifically, we respectively propose two novel game
models for contribution-oblivious FL (COFL) and contribution-aware FL (CAFL),
where the latter one implements a minimum contribution threshold mechanism. We
further analyze the uniqueness and existence for Nash equilibrium of both COFL
and CAFL games and design efficient algorithms to achieve equilibrium
solutions. Extensive performance evaluations show that there exists free-riding
phenomenon in COFL, which can be greatly alleviated through the adoption of
CAFL model with the optimized minimum threshold.

本论文提出了一个新的分析框架来激励参与者中心的联邦学习，分别提出了两种贡献无关的 FL（COFL）和贡献感知 FL（CAFL）游戏模型，讨论了 Nash 平衡的独特性和存在性并设计了有效的算法来达到平衡解决方案。 大量的性能评估表明，COFL 中存在着 free-riding 现象，而采用具有优化的最小阈值的 CAFL 模型可以大大缓解此现象。