Collaborative self-supervised learning has recently become feasible in highly
distributed environments by dividing the network layers between client devices
and a central server. However, state-of-the-art methods, such as MocoSFL, are
optimized for network division at the initial layers, which decreases the
protection of the client data and increases communication overhead. In this
paper, we demonstrate that splitting depth is crucial for maintaining privacy
and communication efficiency in distributed training. We also show that MocoSFL
suffers from a catastrophic quality deterioration for the minimal communication
overhead. As a remedy, we introduce Momentum-Aligned contrastive Split
Federated Learning (MonAcoSFL), which aligns online and momentum client models
during training procedure. Consequently, we achieve state-of-the-art accuracy
while significantly reducing the communication overhead, making MonAcoSFL more
practical in real-world scenarios.

通过分割网络层，将网络层分配给客户端设备和中央服务器，我们介绍了一种名为 MonAcoSFL 的 Momentum-Aligned contrastive Split Federated Learning 方法，通过保持深度分割来维护隐私和通信效率，从而在降低通信开销的同时实现了最先进的准确性。

对分割联邦自监督学习进行深入剖析

A deep cut into Split Federated Self-supervised Learning

Quantum Federated Learning (QFL) has gained significant attention due to
quantum computing and machine learning advancements. As the demand for QFL
continues to surge, there is a pressing need to comprehend its intricacies in
distributed environments. This paper aims to provide a comprehensive overview
of the current state of QFL, addressing a crucial knowledge gap in the existing
literature. We develop ideas for new QFL frameworks, explore diverse use cases
of applications, and consider the critical factors influencing their design.
The technical contributions and limitations of various QFL research projects
are examined while presenting future research directions and open questions for
further exploration.

本文旨在全面概述目前量子联合学习基础，包括了新 QFL 框架的概念、广泛的应用案例和设计的关键因素，并考虑了各种 QFL 研究项目的技术贡献和限制，提出了未来研究方向和开放性问题。

量子联邦学习：分析、设计和实现挑战

Quantum Federated Learning: Analysis, Design and Implementation  Challenges

We consider the estimation of Dirichlet Process Mixture Models (DPMMs) in
distributed environments, where data are distributed across multiple computing
nodes. A key advantage of Bayesian nonparametric models such as DPMMs is that
they allow new components to be introduced on the fly as needed. This, however,
posts an important challenge to distributed estimation -- how to handle new
components efficiently and consistently. To tackle this problem, we propose a
new estimation method, which allows new components to be created locally in
individual computing nodes. Components corresponding to the same cluster will
be identified and merged via a probabilistic consolidation scheme. In this way,
we can maintain the consistency of estimation with very low communication cost.
Experiments on large real-world data sets show that the proposed method can
achieve high scalability in distributed and asynchronous environments without
compromising the mixing performance.

在分布式环境中，通过提出一种新的估计方法，允许本地节点创建新元素并通过概率一致性方案合并相应聚类的组件，同时保持估计的一致性，此法可在分布式和异步环境中实现高可扩展性。

面向分布式数据的狄利克雷过程混合模型可伸缩估计

Scalable Estimation of Dirichlet Process Mixture Models on Distributed  Data

In this chapter, we analyze nonlinear filtering problems in distributed
environments, e.g., sensor networks or peer-to-peer protocols. In these
scenarios, the agents in the environment receive measurements in a streaming
fashion, and they are required to estimate a common (nonlinear) model by
alternating local computations and communications with their neighbors. We
focus on the important distinction between single-task problems, where the
underlying model is common to all agents, and multitask problems, where each
agent might converge to a different model due to, e.g., spatial dependencies or
other factors. Currently, most of the literature on distributed learning in the
nonlinear case has focused on the single-task case, which may be a strong
limitation in real-world scenarios. After introducing the problem and reviewing
the existing approaches, we describe a simple kernel-based algorithm tailored
for the multitask case. We evaluate the proposal on a simulated benchmark task,
and we conclude by detailing currently open problems and lines of research.

本文用于分析分布式情况下的非线性滤波问题，提出了一种针对多任务场景的基于核的算法，并对其进行了模拟基准测试，最后详细说明了目前尚未解决的问题和未来的研究方向。