Personalized Federated Learning (PFL) aims to train machine learning models on decentralized, heterogeneous data while preserving user privacy. This research survey examines the core communication challenges in PFL and evaluates optimization strategies to address key issues, including data heterogeneity, high communication costs, model drift, privacy vulnerabilities, and device variability. We provide a comprehensive analysis of key communication optimization techniques; Model Compression, Differential Privacy, Client Selection, Asynchronous Updates, Gradient Compression, and Model Caching, by their efficiency and effectiveness under diverse PFL conditions. Our study quantitatively compares these methods, identifies limitations, and proposes enhanced strategies to improve communication efficiency, reduce latency, and maintain model accuracy. This research delivers actionable insights for optimizing PFL communication, enhancing both model performance and privacy safeguards. Overall, this work serves as a valuable resource for researchers and practitioners, offering practical guidance on leveraging advanced communication techniques to drive PFL improvements and highlighting promising directions for future research.

中文翻译：

---

个性化联合学习中的通信优化技术：应用、挑战和未来方向


个性化联合学习 （PFL） 旨在基于分散的异构数据训练机器学习模型，同时保护用户隐私。本研究调查研究了 PFL 中的核心通信挑战，并评估了解决关键问题的优化策略，包括数据异构性、高通信成本、模型漂移、隐私漏洞和设备可变性。我们提供对关键通信优化技术的全面分析;模型压缩、差分隐私、客户端选择、异步更新、梯度压缩和模型缓存，以及它们在不同 PFL 条件下的效率和有效性。我们的研究定量比较了这些方法，确定了局限性，并提出了增强的策略，以提高通信效率、减少延迟并保持模型准确性。这项研究为优化 PFL 通信、增强模型性能和隐私保护提供了可操作的见解。总体而言，这项工作为研究人员和从业者提供了宝贵的资源，为利用先进的通信技术推动 PFL 改进提供了实用指导，并为未来研究提供了有希望的方向。