新论文提出SP-CACW，让联邦学习里只选利于你的客户端梯度，避免负迁移，效果比个性化基线还好。
标准联邦学习优化全局平均目标，对数据分布差异大的客户端表现不佳。本文提出SP-CACW框架，通过最小化目标客户端收敛误差的上界来选择聚合权重，可在偏差与方差间权衡并分配零权重给有害客户端。在MNIST、CIFAR-100和LEAF Shakespeare数据集上，该方法与强个性化及聚类基线相比具有竞争力或更优。
SP-CACW: Convergence-Aware Client Weighting for Selfish Personalized Learning
Collaborative learning is sustainable only when it benefits each participant. Standard federated learning optimizes a global average objective, which can under perform for clients whose data distributions differ substantially from the population. We study selfish personalization: how a designated target client can use peer gradients to minimize its own risk while avoiding negative transfer. We propose SP-CACW, a convergence-aware client-weighting framework that selects aggregation weights by minimizing an upper bound on the target client's convergence error. The resulting rule explicitly trades off peer bias against stochastic variance and can assign zero weight to harmful peers. We provide convergence guarantees under smoothness and bounded-variance assumptions and evaluate the method on MNIST, CIFAR-100, and LEAF Shakespeare, where it is competitive with or improves over strong personalized and clustering baselines.