在异构环境下实现个性化联邦学习的高效算法
Federated $\mathcal{X}$-armed Bandit with Flexible Personalisation
- 用代理目标函数融合用户偏好与全局知识，灵活权衡个性化与集体学习
- 提出分阶段淘汰算法，实现次线性后悔率与对数级通信开销
- 适用于医疗、智能家居等需平衡个性化与全局洞察的场景
本文提出一种新型个性化联邦学习方法，基于$$\mathcal{X}$-armed bandit框架，解决在高度异构环境中同时优化本地与全局目标的挑战。该方法采用结合个体客户端偏好与聚合全局知识的代理目标函数，实现个性化与集体学习间的灵活权衡。我们设计了一种分阶段淘汰算法，理论上可达到次线性后悔率，且通信开销为对数级别，适合联邦学习场景。理论分析与实验评估均表明该方法优于现有方法。潜在应用涵盖医疗、智能家居及电子商务等领域，这些领域均需在个性化与全局洞察间取得平衡。
原文摘要 · Abstract (English)
This paper introduces a novel approach to personalised federated learning within the $\mathcal{X}$-armed bandit framework, addressing the challenge of optimising both local and global objectives in a highly heterogeneous environment. Our method employs a surrogate objective function that combines individual client preferences with aggregated global knowledge, allowing for a flexible trade-off between personalisation and collective learning. We propose a phase-based elimination algorithm that achieves sublinear regret with logarithmic communication overhead, making it well-suited for federated settings. Theoretical analysis and empirical evaluations demonstrate the effectiveness of our approach compared to existing methods. Potential applications of this work span various domains, including healthcare, smart home devices, and e-commerce, where balancing personalisation with global insights is crucial.
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