Federated learning (FL) has emerged as a pivotal approach in machine
learning, enabling multiple participants to collaboratively train a global
model without sharing raw data. While FL finds applications in various domains
such as healthcare and finance, it is challenging to ensure global model
convergence when participants do not contribute equally and/or honestly. To
overcome this challenge, principled mechanisms are required to evaluate the
contributions made by individual participants in the FL setting. Existing
solutions for contribution assessment rely on general accuracy evaluation,
often failing to capture nuanced dynamics and class-specific influences. This
paper proposes a novel contribution assessment method called ShapFed for
fine-grained evaluation of participant contributions in FL. Our approach uses
Shapley values from cooperative game theory to provide a granular understanding
of class-specific influences. Based on ShapFed, we introduce a weighted
aggregation method called ShapFed-WA, which outperforms conventional federated
averaging, especially in class-imbalanced scenarios. Personalizing participant
updates based on their contributions further enhances collaborative fairness by
delivering differentiated models commensurate with the participant
contributions. Experiments on CIFAR-10, Chest X-Ray, and Fed-ISIC2019 datasets
demonstrate the effectiveness of our approach in improving utility, efficiency,
and fairness in FL systems. The code can be found at
this https URL

该研究提出了一种名为 ShapFed 的新型参与者贡献评估方法，利用合作博弈论中的 Shapley 值来对参与者的贡献进行精细化评估，并基于此提出了一种名为 ShapFed-WA 的加权聚合方法，尤其在类别不平衡的场景中表现优于传统的联邦平均方法，通过根据参与者的贡献个性化更新模型，进一步提高合作公平性。在 CIFAR-10、Chest X-Ray 和 Fed-ISIC2019 数据集上的实验证明了该方法在提高联邦学习系统的效用、效率和公平性方面的有效性。

重新定义贡献：基于 Shapley 值的联邦学习

Redefining Contributions: Shapley-Driven Federated Learning

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

Federated learning (FL) is an emerging practical framework for effective and
scalable machine learning among multiple participants, such as end users,
organizations and companies. However, most existing FL or distributed learning
frameworks have not well addressed two important issues together: collaborative
fairness and adversarial robustness (e.g. free-riders and malicious
participants). In conventional FL, all participants receive the global model
(equal rewards), which might be unfair to the high-contributing participants.
Furthermore, due to the lack of a safeguard mechanism, free-riders or malicious
adversaries could game the system to access the global model for free or to
sabotage it. In this paper, we propose a novel Robust and Fair Federated
Learning (RFFL) framework to achieve collaborative fairness and adversarial
robustness simultaneously via a reputation mechanism. RFFL maintains a
reputation for each participant by examining their contributions via their
uploaded gradients (using vector similarity) and thus identifies
non-contributing or malicious participants to be removed. Our approach
differentiates itself by not requiring any auxiliary/validation dataset.
Extensive experiments on benchmark datasets show that RFFL can achieve high
fairness and is very robust to different types of adversaries while achieving
competitive predictive accuracy.

本文提出了一种新的稳健公平联邦学习（Robust and Fair Federated Learning，RFFL）框架，通过声誉机制实现协作公平和对抗鲁棒性，可在基准数据集上实现高公正性并对不同类型的对手非常鲁棒。

声誉机制即所需：联邦学习中的协作公平和对抗鲁棒性

A Reputation Mechanism Is All You Need: Collaborative Fairness and  Adversarial Robustness in Federated Learning

In current deep learning paradigms, local training or the Standalone
framework tends to result in overfitting and thus poor generalizability. This
problem can be addressed by Distributed or Federated Learning (FL) that
leverages a parameter server to aggregate model updates from individual
participants. However, most existing Distributed or FL frameworks have
overlooked an important aspect of participation: collaborative fairness. In
particular, all participants can receive the same or similar models, regardless
of their contributions. To address this issue, we investigate the collaborative
fairness in FL, and propose a novel Collaborative Fair Federated Learning
(CFFL) framework which utilizes reputation to enforce participants to converge
to different models, thus achieving fairness without compromising the
predictive performance. Extensive experiments on benchmark datasets demonstrate
that CFFL achieves high fairness, delivers comparable accuracy to the
Distributed framework, and outperforms the Standalone framework.

研究分布式和联邦学习的合作公平性问题，提出了一种新的协作公平联邦学习框架，利用声誉来强制不同参与者收敛于不同模型，从而在不损害预测性能的情况下实现公平。