Prompt learning in pretrained visual-language models has shown remarkable
flexibility across various downstream tasks. Leveraging its inherent
lightweight nature, recent research attempted to integrate the powerful
pretrained models into federated learning frameworks to simultaneously reduce
communication costs and promote local training on insufficient data. Despite
these efforts, current federated prompt learning methods lack specialized
designs to systematically address severe data heterogeneities, e.g., data
distribution with both label and feature shifts involved. To address this
challenge, we present Federated Prompts Cooperation via Optimal Transport
(FedOTP), which introduces efficient collaborative prompt learning strategies
to capture diverse category traits on a per-client basis. Specifically, for
each client, we learn a global prompt to extract consensus knowledge among
clients, and a local prompt to capture client-specific category
characteristics. Unbalanced Optimal Transport is then employed to align local
visual features with these prompts, striking a balance between global consensus
and local personalization. Extensive experiments on datasets with various types
of heterogeneities have demonstrated that our FedOTP outperforms the
state-of-the-art methods.

通过使用最优传输，我们提出了联邦化的提示协作方法（FedOTP），该方法采用高效的合作提示学习策略，以在每个客户端上捕获多样的类别特征，从而在数据异构性方面优于现有方法。

通过最优传输促进联邦学习中的全局和局部协作

Global and Local Prompts Cooperation via Optimal Transport for Federated  Learning

Federated learning (FL) demonstrates its advantages in integrating
distributed infrastructure, communication, computing and learning in a
privacy-preserving manner. However, the robustness and capabilities of existing
FL methods are challenged by limited and dynamic data and conditions,
complexities including heterogeneities and uncertainties, and analytical
explainability. Bayesian federated learning (BFL) has emerged as a promising
approach to address these issues. This survey presents a critical overview of
BFL, including its basic concepts, its relations to Bayesian learning in the
context of FL, and a taxonomy of BFL from both Bayesian and federated
perspectives. We categorize and discuss client- and server-side and FL-based
BFL methods and their pros and cons. The limitations of the existing BFL
methods and the future directions of BFL research further address the intricate
requirements of real-life FL applications.

本文是一项关于贝叶斯联邦学习（BFL）的问题与应用的调查研究，旨在解决分布式基础设施、通信、计算和隐私保护等问题，讨论客户端、服务端和基于 FL 的 BFL 方法，并提出了 BFL 研究的未来发展方向。

贝叶斯联邦学习：综述

Bayesian Federated Learning: A Survey

Machine learning-based wearable human activity recognition (WHAR) models
enable the development of various smart and connected community applications
such as sleep pattern monitoring, medication reminders, cognitive health
assessment, sports analytics, etc. However, the widespread adoption of these
WHAR models is impeded by their degraded performance in the presence of data
distribution heterogeneities caused by the sensor placement at different body
positions, inherent biases and heterogeneities across devices, and personal and
environmental diversities. Various traditional machine learning algorithms and
transfer learning techniques have been proposed in the literature to address
the underpinning challenges of handling such data heterogeneities. Domain
adaptation is one such transfer learning techniques that has gained significant
popularity in recent literature. In this paper, we survey the recent progress
of domain adaptation techniques in the Inertial Measurement Unit (IMU)-based
human activity recognition area, discuss potential future directions.

本文综述了近期就惯性测量单位（IMU）为基础的人体活动识别领域中应用于领域自适应技术的最新研究进展，并讨论了潜在的未来方向。