论文标题
一种用于个性化联合学习的联盟形成游戏方法
A Coalition Formation Game Approach for Personalized Federated Learning
论文作者
论文摘要
在客户本地数据分布中面临统计多样性的挑战，个性化联合学习（PFL）已成为不断增长的研究热点。尽管具有基于模型相似性的成对协作的最先进方法已经达到了有希望的表现，但他们忽略了一个事实，即模型聚合本质上是联盟内部的协作过程，在该过程中，复杂的多方向影响在客户之间发生。在本文中，我们首先将Shapley Value（SV）从联盟游戏理论中应用于PFL方案。为了衡量一组客户在个性化学习绩效方面的多方向协作，SV作为指标对最终结果的边际贡献。我们提出了一种新颖的个性化算法：PFEDSV，它可以1。确定每个客户的最佳合作者联盟和2。基于SV执行个性化模型聚合。通过不同的非IID数据设置（病理和Dirichlet）进行了各种数据集（MNIST，Fashion-MNIST和CIFAR-10）的广泛实验。结果表明，与最新的基准相比，PFEDSV可以为每个客户实现卓越的个性化精度。
Facing the challenge of statistical diversity in client local data distribution, personalized federated learning (PFL) has become a growing research hotspot. Although the state-of-the-art methods with model similarity-based pairwise collaboration have achieved promising performance, they neglect the fact that model aggregation is essentially a collaboration process within the coalition, where the complex multiwise influences take place among clients. In this paper, we first apply Shapley value (SV) from coalition game theory into the PFL scenario. To measure the multiwise collaboration among a group of clients on the personalized learning performance, SV takes their marginal contribution to the final result as a metric. We propose a novel personalized algorithm: pFedSV, which can 1. identify each client's optimal collaborator coalition and 2. perform personalized model aggregation based on SV. Extensive experiments on various datasets (MNIST, Fashion-MNIST, and CIFAR-10) are conducted with different Non-IID data settings (Pathological and Dirichlet). The results show that pFedSV can achieve superior personalized accuracy for each client, compared to the state-of-the-art benchmarks.
