In the evolving application of medical artificial intelligence, federated
learning is notable for its ability to protect training data privacy. Federated
learning facilitates collaborative model development without the need to share
local data from healthcare institutions. Yet, the statistical and system
heterogeneity among these institutions poses substantial challenges, which
affects the effectiveness of federated learning and hampers the exchange of
information between clients. To address these issues, we introduce a novel
approach, MH-pFLGB, which employs a global bypass strategy to mitigate the
reliance on public datasets and navigate the complexities of non-IID data
distributions. Our method enhances traditional federated learning by
integrating a global bypass model, which would share the information among the
clients, but also serves as part of the network to enhance the performance on
each client. Additionally, MH-pFLGB provides a feature fusion module to better
combine the local and global features. We validate \model{}'s effectiveness and
adaptability through extensive testing on different medical tasks,
demonstrating superior performance compared to existing state-of-the-art
methods.

在医学人工智能应用中，联邦学习以保护训练数据隐私而著名。我们引入了一种新方法 MH-pFLGB，利用全局旁路策略来解决统计和系统异质性带来的挑战，并优化联邦学习的有效性。此方法通过整合全局旁路模型，增强了传统联邦学习的性能，同时还提供特征融合模块以更好地结合本地和全局特征，在不同的医学任务中展示出卓越的性能。

MH-pFLGB: 模型异构个性化全球绕行联邦学习用于医学图像分析

MH-pFLGB: Model Heterogeneous personalized Federated Learning via Global  Bypass for Medical Image Analysis

Federated learning (FL) is a key enabler for efficient communication and
computing, leveraging devices' distributed computing capabilities. However,
applying FL in practice is challenging due to the local devices' heterogeneous
energy, wireless channel conditions, and non-independently and identically
distributed (non-IID) data distributions. To cope with these issues, this paper
proposes a novel learning framework by integrating FL and width-adjustable
slimmable neural networks (SNN). Integrating FL with SNNs is challenging due to
time-varying channel conditions and data distributions. In addition, existing
multi-width SNN training algorithms are sensitive to the data distributions
across devices, which makes SNN ill-suited for FL. Motivated by this, we
propose a communication and energy-efficient SNN-based FL (named SlimFL) that
jointly utilizes superposition coding (SC) for global model aggregation and
superposition training (ST) for updating local models. By applying SC, SlimFL
exchanges the superposition of multiple-width configurations decoded as many
times as possible for a given communication throughput. Leveraging ST, SlimFL
aligns the forward propagation of different width configurations while avoiding
inter-width interference during backpropagation. We formally prove the
convergence of SlimFL. The result reveals that SlimFL is not only
communication-efficient but also deals with non-IID data distributions and poor
channel conditions, which is also corroborated by data-intensive simulations.

本文提出了一种新的基于 slimmable 神经网络的联邦学习框架（SlimFL），通过同时利用超级编码（SC）进行全局模型聚合和超级训练（ST）进行本地模型更新来交换多种宽度配置的超级位置解码，从而实现在通信效率和能量效率方面都较好并且能够处理非独立同分布数据分布和不良信道条件。

SlimFL：基于可减缩神经网络超定编码的联邦学习

SlimFL: Federated Learning with Superposition Coding over Slimmable Neural Networks

We propose using federated learning, a decentralized on-device learning
paradigm, to train speech recognition models. By performing epochs of training
on a per-user basis, federated learning must incur the cost of dealing with
non-IID data distributions, which are expected to negatively affect the quality
of the trained model. We propose a framework by which the degree of
non-IID-ness can be varied, consequently illustrating a trade-off between model
quality and the computational cost of federated training, which we capture
through a novel metric. Finally, we demonstrate that hyper-parameter
optimization and appropriate use of variational noise are sufficient to
compensate for the quality impact of non-IID distributions, while decreasing
the cost.

本文提出使用联邦学习来训练语音识别模型，并通过对非独立同分布数据分布程度的调整来平衡模型质量和联邦训练计算成本之间的关系，并证明超参数优化和适当使用变分噪声可以弥补非独立同分布数据对模型影响的影响。