主要內容：

　　不同於梯度壓縮和模型壓縮，FedBoost整合學習演算法，能夠降低伺服器到客戶端

　　和客戶端到伺服器的通訊成本，提高通訊效率。

整合學習：整合學習(ensemble learning)原理詳解_春華秋實-CSDN部落格_整合學習

主要優點：

　　1. Pre-trained base predictors: base predictors can be pre-trained on publicly available data,

　　　thus reducing the need for user data in training.

　　2.Convergence guarantee: ensemble methods often require training relatively few parameters,

　　　which typically results in far fewer rounds of optimization and faster convergence compared to

　　　training the entire model from scratch.

　　3.Adaptation or drifting over time: user data may change over time, but, in the ensemble approach,

　　　we can keep the base predictors fixed and retrain the ensemble weights whenever the data changes.

　　4.Differential privacy (DP): federated learning can be combined with global DP to provide an additional

　　　layer of privacy . Training only the ensemble weights via federated learning is well-suited for DP since

　　　the utility-privacy trade-off depends on the number of parameters being trained . Furthermore, this

　　　learning problem is typically a convex optimization problem for which DP convex optimization can give

　　　better privacy guarantees.