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。

FedSAC：联邦学习中协同公平的动态子模型分配

FedSAC: Dynamic Submodel Allocation for Collaborative Fairness in  Federated Learning

Incentive mechanism is crucial for federated learning (FL) when rational
clients do not have the same interests in the global model as the server.
However, due to system heterogeneity and limited budget, it is generally
impractical for the server to incentivize all clients to participate in all
training rounds (known as full participation). The existing FL incentive
mechanisms are typically designed by stimulating a fixed subset of clients
based on their data quantity or system resources. Hence, FL is performed only
using this subset of clients throughout the entire training process, leading to
a biased model because of data heterogeneity. This paper proposes a game
theoretic incentive mechanism for FL with randomized client participation,
where the server adopts a customized pricing strategy that motivates different
clients to join with different participation levels (probabilities) for
obtaining an unbiased and high performance model. Each client responds to the
server's monetary incentive by choosing its best participation level, to
maximize its profit based on not only the incurred local cost but also its
intrinsic value for the global model. To effectively evaluate clients'
contribution to the model performance, we derive a new convergence bound which
analytically predicts how clients' arbitrary participation levels and their
heterogeneous data affect the model performance. By solving a non-convex
optimization problem, our analysis reveals that the intrinsic value leads to
the interesting possibility of bidirectional payment between the server and
clients. Experimental results using real datasets on a hardware prototype
demonstrate the superiority of our mechanism in achieving higher model
performance for the server as well as higher profits for the clients.

本文提出了一种基于博弈论的激励机制，通过随机客户端参与度数，实现以不偏差的方式获得高性能模型，并分析了客户端的参与度数和异质性数据如何影响模型性能的收敛界。