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 this https URL

FedSAC 是一种具有动态子模型分配的基于协作公平性的新型联邦学习框架，通过个体贡献来量身定制奖励，实现公平性，并采用子模型分配和动态聚合模块来提高整体模型准确性。