用动态图注意力混合专家实现个性化联邦预测，提升电动车充电负荷预报精度。
Fed-GAME: Personalized Federated Learning with Graph Attention Mixture-of-Experts For Time-Series Forecasting
- 通过参数差分传递与动态隐式图建模，实现客户端个性化聚合。
- 在两个真实电动车充电数据集上，性能优于现有最优个性化联邦方法。
- 适合处理数据异构性强的时序预测任务，如能源负荷预测。
图上的联邦学习在分布式时序预测中展现出潜力，但现有方法依赖静态拓扑，难以应对客户端异质性。本文提出 Fed-GAME 框架，将个性化聚合建模为可学习的动态隐式图上的消息传递。核心是基于参数差异的解耦更新协议：客户端传输微调后私有模型与共享全局模型之间的参数差分。服务器端将这些差分分解为两路：(1) 平均差分用于更新全局模型以达成共识；(2) 选择性差分输入新型图注意力混合专家（GAME）聚合器，实现细粒度个性化。该聚合器中，共享专家提供评分信号，个性化门控自适应加权选择性更新，支持个性化聚合。在两个真实世界电动车充电数据集上的实验表明，Fed-GAME 在性能上优于当前最先进的个性化联邦学习基线。
原文摘要 · Abstract (English)
Federated learning (FL) on graphs shows promise for distributed time-series forecasting. Yet, existing methods rely on static topologies and struggle with client heterogeneity. We propose Fed-GAME, a framework that models personalized aggregation as message passing over a learnable dynamic implicit graph. The core is a decoupled parameter difference-based update protocol, where clients transmit parameter differences between their fine-tuned private model and a shared global model. On the server, these differences are decomposed into two streams: (1) averaged difference used to updating the global model for consensus (2) the selective difference fed into a novel Graph Attention Mixture-of-Experts (GAME) aggregator for fine-grained personalization. In this aggregator, shared experts provide scoring signals while personalized gates adaptively weight selective updates to support personalized aggregation. Experiments on two real-world electric vehicle charging datasets demonstrate that Fed-GAME outperforms state-of-the-art personalized FL baselines.
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