Collaborative fairness stands as an essential element in federated learning to encourage client participation by equitably distributing rewards based on individual contributions. Existing methods primarily focus on adjusting gradient allocations among clients to achieve collaborative fairness. However, they frequently overlook crucial factors such as maintaining consistency across local models and catering to the diverse requirements of high-contributing clients. This oversight inevitably decreases both fairness and model accuracy in practice. To address these issues, we propose FedSAC, a novel Federated learning framework with dynamic Submodel Allocation for Collaborative fairness, backed by a theoretical convergence guarantee. First, we present the concept of "bounded collaborative fairness (BCF)", which ensures fairness by tailoring rewards to individual clients based on their contributions. Second, to implement the BCF, we design a submodel allocation module with a theoretical guarantee of fairness. This module incentivizes high-contributing clients with high-performance submodels containing a diverse range of crucial neurons, thereby preserving consistency across local models. Third, we further develop a dynamic aggregation module to adaptively aggregate submodels, ensuring the equitable treatment of low-frequency neurons and consequently enhancing overall model accuracy. Extensive experiments conducted on three public benchmarks demonstrate that FedSAC outperforms all baseline methods in both fairness and model accuracy. We see this work as a significant step towards incentivizing broader client participation in federated learning. The source code is available at https://github.com/wangzihuixmu/FedSAC.

翻译：协作公平性是联邦学习中的关键要素，旨在通过依据个体贡献公平分配奖励来激励客户端参与。现有方法主要侧重于调整客户端间的梯度分配以实现协作公平性，但往往忽视了维持局部模型间一致性以及满足高贡献客户端多样化需求等关键因素。这种疏忽在实践中不可避免地降低了公平性与模型精度。为解决这些问题，我们提出FedSAC——一种具备理论收敛保证、通过动态子模型分配实现协作公平性的新型联邦学习框架。首先，我们提出“有界协作公平性（BCF）”概念，通过依据个体贡献为不同客户端定制化奖励来确保公平性。其次，为实现BCF，我们设计了一个具有理论公平性保证的子模型分配模块。该模块通过向高贡献客户端分配包含多样化关键神经元的高性能子模型作为激励，从而保持局部模型间的一致性。第三，我们进一步开发了动态聚合模块来自适应地聚合子模型，确保对低频神经元的公平处理，进而提升整体模型精度。在三个公开基准数据集上的大量实验表明，FedSAC在公平性与模型精度方面均优于所有基线方法。我们认为这项工作对于激励更广泛的客户端参与联邦学习具有重要意义。源代码发布于 https://github.com/wangzihuixmu/FedSAC。