标题： 一种在概念漂移下的联邦学习的信息论分析 显示英文标题

标题： An Information-Theoretic Analysis for Federated Learning under Concept Drift

摘要： 最近在联邦学习（FL）中的研究通常在静态数据集上训练模型。 然而，现实世界的数据往往以流的形式出现，分布会发生变化，导致性能下降，这种现象称为概念漂移。 本文使用信息论分析了概念漂移下的FL性能，并提出了一种算法来缓解性能下降。 我们将概念漂移建模为马尔可夫链，并引入\emph{平稳推广 误差}来评估模型捕捉未来未见数据特征的能力。 其上界通过KL散度和互信息推导得出。 我们研究了三种漂移模式（周期性、渐进性和随机性）及其对FL性能的影响。 受此启发，我们提出了一种算法，通过KL散度和互信息对经验风险最小化方法进行正则化，从而提高长期性能。 我们还通过识别帕累托前沿探讨了性能与成本之间的权衡。 为了验证我们的方法，我们使用树莓派4设备构建了一个FL测试平台。 实验结果与理论发现一致，证实了漂移模式对性能有显著影响。 我们的方法在这三种模式下始终优于现有方法，证明了其在适应FL中概念漂移方面的有效性。 显示英文摘要

摘要： Recent studies in federated learning (FL) commonly train models on static datasets. However, real-world data often arrives as streams with shifting distributions, causing performance degradation known as concept drift. This paper analyzes FL performance under concept drift using information theory and proposes an algorithm to mitigate the performance degradation. We model concept drift as a Markov chain and introduce the \emph{Stationary Generalization Error} to assess a model's capability to capture characteristics of future unseen data. Its upper bound is derived using KL divergence and mutual information. We study three drift patterns (periodic, gradual, and random) and their impact on FL performance. Inspired by this, we propose an algorithm that regularizes the empirical risk minimization approach with KL divergence and mutual information, thereby enhancing long-term performance. We also explore the performance-cost tradeoff by identifying a Pareto front. To validate our approach, we build an FL testbed using Raspberry Pi4 devices. Experimental results corroborate with theoretical findings, confirming that drift patterns significantly affect performance. Our method consistently outperforms existing approaches for these three patterns, demonstrating its effectiveness in adapting concept drift in FL.