HDC-FL：用分类器头聚类实现个性化联邦学习
From Classifier Heads to Encoder Prototypes: Personalized Federated Learning with Head-Derived Clustering
一篇联邦学习论文，用现成的分类器头当聚类信号，不用加额外优化，在最差客户端准确率上跑赢了对比方法。
arXiv 论文提出 HDC-FL，利用联邦学习中客户端特有的分类器头作为聚类信号来决定表示共享。服务器在周期性刷新轮次中将分类器头投影到低秩描述符空间进行分组，为每组构建编码器原型，客户端接收原型的软混合。在 11 个基于 CIFAR-10 和 CIFAR-100 的配置上，使用 ResNet-18 和 MobileNetV2 进行测试，HDC-FL 在最差客户端和后 20% 客户端的宏观平均准确率上达到数值最高。
From Classifier Heads to Encoder Prototypes: Personalized Federated Learning with Head-Derived Clustering
Personalized federated learning must decide which clients should share representations. We introduce HDC-FL (Head-Derived Clustering for Personalized Federated Learning), which reads that decision off a signal shared-encoder training already produces: the client-specific classifi- er heads. At periodic refresh rounds the server projects the cached heads into a normalized low-rank descriptor space, groups the descriptors, builds one encoder prototype per group from the cached client encoders, and returns to each client a soft mixture of those prototypes paired with its own unchanged head. Client-specific heads are never averaged or distributed to oth- er clients; the shared-encoder trajectory is left intact; and no second optimization trajectory, additional local objective, or post-training fine-tuning stage is required. Across 11 CIFAR-10 and CIFAR-100 configurations covering label-subset, Dirichlet and rotation heterogeneity with ResNet-18 and MobileNetV2 encoders, HDC-FL is competitive with the strongest personalized and clustered baselines in macro-averaged mean accuracy and attains the numerically highest macro-averaged worst-client and bottom-20% accuracy among the evaluated methods. The tail results distinguish methods that look similar when only their means are compared. A controlled donor-swap diagnostic further finds positive donor-ranking associations between head similarity and encoder compatibility in all three settings tested, most clearly under covariate shift.