对比10种个性化联邦学习方法，发现本地聚合最快最省资源。
Personalized Federated Learning Techniques: Empirical Analysis
- 采用本地聚合的pFL方法通信计算更高效，收敛最快。
- 微调方法在数据异构下表现差，易受攻击，准确率低。
- 多目标学习精度高但耗时长，适合资源充足场景。
个性化联邦学习（pFL）在保护数据隐私的同时为个体用户提供定制化模型，具有巨大潜力。然而，实现pFL最优性能需权衡内存开销与模型精度之间的矛盾。本文通过实证分析十种主流pFL技术在多个数据集和数据划分下的表现，揭示了显著差异。研究发现，采用个性化（本地）聚合的方法因通信与计算效率高，收敛速度最快；而微调方法在处理数据异构性和潜在对抗攻击方面存在局限；多目标学习方法虽能获得更高精度，但需额外训练时间与资源消耗。研究强调通信效率在规模化pFL部署中的关键作用，其对实际应用中的资源使用有显著影响。
原文摘要 · Abstract (English)
Personalized Federated Learning (pFL) holds immense promise for tailoring machine learning models to individual users while preserving data privacy. However, achieving optimal performance in pFL often requires a careful balancing act between memory overhead costs and model accuracy. This paper delves into the trade-offs inherent in pFL, offering valuable insights for selecting the right algorithms for diverse real-world scenarios. We empirically evaluate ten prominent pFL techniques across various datasets and data splits, uncovering significant differences in their performance. Our study reveals interesting insights into how pFL methods that utilize personalized (local) aggregation exhibit the fastest convergence due to their efficiency in communication and computation. Conversely, fine-tuning methods face limitations in handling data heterogeneity and potential adversarial attacks while multi-objective learning methods achieve higher accuracy at the cost of additional training and resource consumption. Our study emphasizes the critical role of communication efficiency in scaling pFL, demonstrating how it can significantly affect resource usage in real-world deployments.
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