Traditional federated learning (FL) methods have limited support for clients
with varying computational and communication abilities, leading to
inefficiencies and potential inaccuracies in model training. This limitation
hinders the widespread adoption of FL in diverse and resource-constrained
environments, such as those with client devices ranging from powerful servers
to mobile devices. To address this need, this paper introduces Federated
Learning with Flexible Architectures (FedFA), an FL training algorithm that
allows clients to train models of different widths and depths. Each client can
select a network architecture suitable for its resources, with shallower and
thinner networks requiring fewer computing resources for training. Unlike prior
work in this area, FedFA incorporates the layer grafting technique to align
clients' local architectures with the largest network architecture in the FL
system during model aggregation. Layer grafting ensures that all client
contributions are uniformly integrated into the global model, thereby
minimizing the risk of any individual client's data skewing the model's
parameters disproportionately and introducing security benefits. Moreover,
FedFA introduces the scalable aggregation method to manage scale variations in
weights among different network architectures. Experimentally, FedFA
outperforms previous width and depth flexible aggregation strategies.
Furthermore, FedFA demonstrates increased robustness against performance
degradation in backdoor attack scenarios compared to earlier strategies.

传统的联邦学习方法对计算和通信能力不同的客户端的支持有限，导致模型训练的低效和潜在的不准确性。本文引入了具有灵活架构的联邦学习算法（FedFA），允许客户端选择适合其资源的网络架构，通过层嫁接技术和可扩展聚合方法，实现了客户端贡献的统一集成和对抗性能降低的增强。