Federated learning-assisted edge intelligence enables privacy protection in
modern intelligent services. However, not Independent and Identically
Distributed (non-IID) distribution among edge clients can impair the local
model performance. The existing single prototype-based strategy represents a
sample by using the mean of the feature space. However, feature spaces are
usually not clustered, and a single prototype may not represent a sample well.
Motivated by this, this paper proposes a multi-prototype federated contrastive
learning approach (MP-FedCL) which demonstrates the effectiveness of using a
multi-prototype strategy over a single-prototype under non-IID settings,
including both label and feature skewness. Specifically, a multi-prototype
computation strategy based on \textit{k-means} is first proposed to capture
different embedding representations for each class space, using multiple
prototypes ($k$ centroids) to represent a class in the embedding space. In each
global round, the computed multiple prototypes and their respective model
parameters are sent to the edge server for aggregation into a global prototype
pool, which is then sent back to all clients to guide their local training.
Finally, local training for each client minimizes their own supervised learning
tasks and learns from shared prototypes in the global prototype pool through
supervised contrastive learning, which encourages them to learn knowledge
related to their own class from others and reduces the absorption of unrelated
knowledge in each global iteration. Experimental results on MNIST, Digit-5,
Office-10, and DomainNet show that our method outperforms multiple baselines,
with an average test accuracy improvement of about 4.6\% and 10.4\% under
feature and label non-IID distributions, respectively.

本文提出了一种使用多原型策略的联邦对比学习方法 (MP-FedCL)，旨在增强分散式智能服务中的隐私保护功能，并在标签和特征分布不独立且倾斜的情况下，证明了多原型策略的有效性，并在 MNIST，Digit-5，Office-10 和 DomainNet 等多个数据集上显示出优异的性能.

MP-FedCL：面向边缘智能的多原型联合对比学习

MP-FedCL: Multi-Prototype Federated Contrastive Learning for Edge  Intelligence

Many existing approaches for 3D point cloud semantic segmentation are fully
supervised. These fully supervised approaches heavily rely on large amounts of
labeled training data that are difficult to obtain and cannot segment new
classes after training. To mitigate these limitations, we propose a novel
attention-aware multi-prototype transductive few-shot point cloud semantic
segmentation method to segment new classes given a few labeled examples.
Specifically, each class is represented by multiple prototypes to model the
complex data distribution of labeled points. Subsequently, we employ a
transductive label propagation method to exploit the affinities between labeled
multi-prototypes and unlabeled points, and among the unlabeled points.
Furthermore, we design an attention-aware multi-level feature learning network
to learn the discriminative features that capture the geometric dependencies
and semantic correlations between points. Our proposed method shows significant
and consistent improvements compared to baselines in different few-shot point
cloud semantic segmentation settings (i.e., 2/3-way 1/5-shot) on two benchmark
datasets. Our code is available at this https URL

为了解决 3D 点云语义分割中数据有限和模型泛化性能差的问题，本文提出了一种基于注意力机制多突出物体的跨导语义分割方法，并在各种 Few-shot 点云语义分割场景下取得了显著的效果提升。