Cross-silo federated learning (FL) enables decentralized organizations to
collaboratively train models while preserving data privacy and has made
significant progress in medical image classification. One common assumption is
task homogeneity where each client has access to all classes during training.
However, in clinical practice, given a multi-label classification task,
constrained by the level of medical knowledge and the prevalence of diseases,
each institution may diagnose only partial categories, resulting in task
heterogeneity. How to pursue effective multi-label medical image classification
under task heterogeneity is under-explored. In this paper, we first formulate
such a realistic label missing setting in the multi-label FL domain and propose
a two-stage method FedMLP to combat class missing from two aspects: pseudo
label tagging and global knowledge learning. The former utilizes a warmed-up
model to generate class prototypes and select samples with high confidence to
supplement missing labels, while the latter uses a global model as a teacher
for consistency regularization to prevent forgetting missing class knowledge.
Experiments on two publicly-available medical datasets validate the superiority
of FedMLP against the state-of-the-art both federated semi-supervised and noisy
label learning approaches under task heterogeneity. Code is available at
this https URL

在多标签联邦学习中，考虑到任务异质性，本文提出了 FedMLP 方法，通过伪标签标记和全局知识学习两个方面来解决标签缺失问题，并在两个公开的医学数据集上验证了其优越性。

FedMLP：面向任务异质性的联邦多标签医学图像分类

FedMLP: Federated Multi-Label Medical Image Classification under Task  Heterogeneity

Few-Shot Text Classification (FSTC) imitates humans to learn a new text
classifier efficiently with only few examples, by leveraging prior knowledge
from historical tasks. However, most prior works assume that all the tasks are
sampled from a single data source, which cannot adapt to real-world scenarios
where tasks are heterogeneous and lie in different distributions. As such,
existing methods may suffer from their globally knowledge-shared mechanisms to
handle the task heterogeneity. On the other hand, inherent task relation are
not explicitly captured, making task knowledge unorganized and hard to transfer
to new tasks. Thus, we explore a new FSTC setting where tasks can come from a
diverse range of data sources. To address the task heterogeneity, we propose a
self-supervised hierarchical task clustering (SS-HTC) method. SS-HTC not only
customizes cluster-specific knowledge by dynamically organizing heterogeneous
tasks into different clusters in hierarchical levels but also disentangles
underlying relations between tasks to improve the interpretability. Extensive
experiments on five public FSTC benchmark datasets demonstrate the
effectiveness of SS-HTC.

本文提出了一种自我监督的层次任务聚类 (SS-HTC) 方法，通过动态地将异构任务组织到不同层次的簇中来自定义聚类特异性知识，并分离任务之间的潜在关系以提高可解释性，以应对任务异构性，实验结果表明 SS-HTC 的有效性。

通过自监督分层任务聚类解耦少样本文本分类任务关系

Disentangling Task Relations for Few-shot Text Classification via Self-Supervised Hierarchical Task Clustering

Increasingly, scientific discovery requires sophisticated and scalable
workflows. Workflows have become the ``new applications,'' wherein multi-scale
computing campaigns comprise multiple and heterogeneous executable tasks. In
particular, the introduction of AI/ML models into the traditional HPC workflows
has been an enabler of highly accurate modeling, typically reducing
computational needs compared to traditional methods. This chapter discusses
various modes of integrating AI/ML models to HPC computations, resulting in
diverse types of AI-coupled HPC workflows. The increasing need of coupling
AI/ML and HPC across scientific domains is motivated, and then exemplified by a
number of production-grade use cases for each mode. We additionally discuss the
primary challenges of extreme-scale AI-coupled HPC campaigns -- task
heterogeneity, adaptivity, performance -- and several framework and middleware
solutions which aim to address them. While both HPC workflow and AI/ML
computing paradigms are independently effective, we highlight how their
integration, and ultimate convergence, is leading to significant improvements
in scientific performance across a range of domains, ultimately resulting in
scientific explorations otherwise unattainable.

通过将人工智能 / 机器学习模型集成到传统超级计算机工作流中，这篇论文讨论了提高科学性能的多种方式和生产级用例，并介绍了解决极限规模 AI 结合 HPC 活动的主要挑战和几种框架和中间件方案。

AI-coupled HPC 工作流程

AI-coupled HPC Workflows

In order to efficiently learn with small amount of data on new tasks,
meta-learning transfers knowledge learned from previous tasks to the new ones.
However, a critical challenge in meta-learning is the task heterogeneity which
cannot be well handled by traditional globally shared meta-learning methods. In
addition, current task-specific meta-learning methods may either suffer from
hand-crafted structure design or lack the capability to capture complex
relations between tasks. In this paper, motivated by the way of knowledge
organization in knowledge bases, we propose an automated relational
meta-learning (ARML) framework that automatically extracts the cross-task
relations and constructs the meta-knowledge graph. When a new task arrives, it
can quickly find the most relevant structure and tailor the learned structure
knowledge to the meta-learner. As a result, the proposed framework not only
addresses the challenge of task heterogeneity by a learned meta-knowledge
graph, but also increases the model interpretability. We conduct extensive
experiments on 2D toy regression and few-shot image classification and the
results demonstrate the superiority of ARML over state-of-the-art baselines.

本篇论文提出一种基于知识库组织方式的自动关系元学习（ARML）框架，它可以从先前完成的任务中自动抽取跨任务关系并构建元知识图，通过学习的元知识图解决任务异质性的挑战，同时提高模型可解释性。