Federated learning (FL) inevitably confronts the challenge of system heterogeneity in practical scenarios. To enhance the capabilities of most model-homogeneous FL methods in handling system heterogeneity, we propose a training scheme that can extend their capabilities to cope with this challenge. In this paper, we commence our study with a detailed exploration of homogeneous and heterogeneous FL settings and discover three key observations: (1) a positive correlation between client performance and layer similarities, (2) higher similarities in the shallow layers in contrast to the deep layers, and (3) the smoother gradients distributions indicate the higher layer similarities. Building upon these observations, we propose InCo Aggregation that leverags internal cross-layer gradients, a mixture of gradients from shallow and deep layers within a server model, to augment the similarity in the deep layers without requiring additional communication between clients. Furthermore, our methods can be tailored to accommodate model-homogeneous FL methods such as FedAvg, FedProx, FedNova, Scaffold, and MOON, to expand their capabilities to handle the system heterogeneity. Copious experimental results validate the effectiveness of InCo Aggregation, spotlighting internal cross-layer gradients as a promising avenue to enhance the performance in heterogenous FL.

翻译：联邦学习（FL）在实际场景中不可避免地面临系统异质性的挑战。为增强大多数模型同质FL方法应对系统异质性的能力，我们提出一种可扩展其能力以应对该挑战的训练方案。本文通过深入探究同质与异质FL设置，发现三个关键现象：（1）客户端性能与层相似性呈正相关，（2）相较于深层，浅层具有更高相似性，（3）梯度分布越平滑，层相似性越高。基于这些发现，我们提出InCo聚合方法，利用服务器模型内部跨层梯度（即浅层与深层梯度的混合），在不增加客户端间通信的情况下增强深层相似性。此外，我们的方法可适配FedAvg、FedProx、FedNova、Scaffold及MOON等模型同质FL方法，扩展其处理系统异质性的能力。大量实验结果验证了InCo聚合的有效性，凸显内部跨层梯度作为提升异质FL性能的重要研究方向。