提出可应对数据异构的联邦随机森林方法，提升隐私保护下的预测性能。
Principled Federated Random Forests for Heterogeneous Data
- 通过聚合客户端统计量实现分步分裂，逼近集中式最优分割。
- 在多种数据异构场景下，性能接近集中式随机森林，通信开销低。
- 支持基于客户端标识的非参数个性化，适用于隐私敏感场景。
随机森林（RF）是中心化表格数据中强大且广泛应用的预测模型，但现有方法难以将其适配到联邦学习场景。由于RF具有分段常数特性，无法进行精确的梯度优化，因此现有联邦RF方法依赖非原则性启发式策略：例如独立训练各客户端决策树后聚合，无法优化全局不纯度准则，即使在简单分布偏移下也表现不佳。本文提出FedForest，一种适用于水平划分数据的新型联邦随机森林算法，能自然处理多样化的客户端数据异构性，涵盖协变量偏移及更复杂的输出偏移机制。我们证明，基于聚合精心选择的客户端统计量的分裂过程，能紧密逼近集中式算法所选分裂。此外，FedForest支持基于客户端标识的分裂，实现先前联邦随机森林方法缺失的非参数个性化。实验证明，该方法在多种异构基准上表现接近集中式性能，同时保持通信高效。
原文摘要 · Abstract (English)
Random Forests (RF) are among the most powerful and widely used predictive models for centralized tabular data, yet few methods exist to adapt them to the federated learning setting. Unlike most federated learning approaches, the piecewise-constant nature of RF prevents exact gradient-based optimization. As a result, existing federated RF implementations rely on unprincipled heuristics: for instance, aggregating decision trees trained independently on clients fails to optimize the global impurity criterion, even under simple distribution shifts. We propose FedForest, a new federated RF algorithm for horizontally partitioned data that naturally accommodates diverse forms of client data heterogeneity, from covariate shift to more complex outcome shift mechanisms. We prove that our splitting procedure, based on aggregating carefully chosen client statistics, closely approximates the split selected by a centralized algorithm. Moreover, FedForest allows splits on client indicators, enabling a non-parametric form of personalization that is absent from prior federated random forest methods. Empirically, we demonstrate that the resulting federated forests closely match centralized performance across heterogeneous benchmarks while remaining communication-efficient.
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