标题： pFedWN：一种用于具有异构数据的D2D无线网络的个性化联邦学习框架 显示英文标题

标题： pFedWN: A Personalized Federated Learning Framework for D2D Wireless Networks with Heterogeneous Data

摘要： 传统联邦学习（FL）方法通常在客户端之间的数据异质性上遇到困难，导致单个客户端的模型性能不佳。 为了解决这个问题，个性化联邦学习（PFL）作为一种解决方案，应对客户端之间非独立同分布（non-IID）和不平衡数据带来的挑战。 此外，在大多数现有的去中心化机器学习工作中，假设客户端和服务器之间的模型参数传输具有完美的通信信道。 然而，在无线链路上的去中心化PFL引入了新的挑战，例如资源分配和干扰管理。 为了克服这些挑战，我们制定一个联合优化问题，将底层设备到设备（D2D）无线信道状况纳入无服务器的PFL方法中。 所提出的方法，称为pFedWN，在考虑D2D无线信道变化的同时，优化每个客户端的学习性能。 为了解决制定的问题，我们将其分为两个子问题：PFL邻居选择和PFL权重分配。 通过在授权频谱带（如ISM频段）内的信道感知邻居选择来解决PFL邻居选择问题。 接下来，为了分配PFL权重，我们利用期望最大化（EM）方法评估客户端数据之间的相似性，并在选定的PFL邻居之间获得最优权重分配。 实证结果表明，pFedWN在非独立同分布和不平衡数据集上提供了高效且个性化的学习性能。 此外，它在学习效果和鲁棒性方面优于现有的FL和PFL方法，特别是在动态和不可预测的无线信道条件下。 显示英文摘要

摘要： Traditional Federated Learning (FL) approaches often struggle with data heterogeneity across clients, leading to suboptimal model performance for individual clients. To address this issue, Personalized Federated Learning (PFL) emerges as a solution to the challenges posed by non-independent and identically distributed (non-IID) and unbalanced data across clients. Furthermore, in most existing decentralized machine learning works, a perfect communication channel is considered for model parameter transmission between clients and servers. However, decentralized PFL over wireless links introduces new challenges, such as resource allocation and interference management. To overcome these challenges, we formulate a joint optimization problem that incorporates the underlying device-to-device (D2D) wireless channel conditions into a server-free PFL approach. The proposed method, dubbed pFedWN, optimizes the learning performance for each client while accounting for the variability in D2D wireless channels. To tackle the formulated problem, we divide it into two sub-problems: PFL neighbor selection and PFL weight assignment. The PFL neighbor selection is addressed through channel-aware neighbor selection within unlicensed spectrum bands such as ISM bands. Next, to assign PFL weights, we utilize the Expectation-Maximization (EM) method to evaluate the similarity between clients' data and obtain optimal weight distribution among the chosen PFL neighbors. Empirical results show that pFedWN provides efficient and personalized learning performance with non-IID and unbalanced datasets. Furthermore, it outperforms the existing FL and PFL methods in terms of learning efficacy and robustness, particularly under dynamic and unpredictable wireless channel conditions.