In modern decentralized applications, ensuring communication efficiency and
privacy for the users are the key challenges. In order to train
machine-learning models, the algorithm has to communicate to the data center
and sample data for its gradient computation, thus exposing the data and
increasing the communication cost. This gives rise to the need for a
decentralized optimization algorithm that is communication-efficient and
minimizes the number of gradient computations. To this end, we propose the
primal-dual sliding with conditional gradient sliding framework, which is
communication-efficient and achieves an $\varepsilon$-approximate solution with
the optimal gradient complexity of
$O(1/\sqrt{\varepsilon}+\sigma^2/{\varepsilon^2})$ and
$O(\log(1/\varepsilon)+\sigma^2/\varepsilon)$ for the convex and strongly
convex setting respectively and an LO (Linear Optimization) complexity of
$O(1/\varepsilon^2)$ for both settings given a stochastic gradient oracle with
variance $\sigma^2$. Compared with the prior work \cite{wai-fw-2017}, our
framework relaxes the assumption of the optimal solution being a strict
interior point of the feasible set and enjoys wider applicability for
large-scale training using a stochastic gradient oracle. We also demonstrate
the efficiency of our algorithms with various numerical experiments.

现代分散应用中，通信效率和用户隐私是关键挑战。为了训练机器学习模型，算法必须与数据中心进行通信并对其梯度计算进行采样，从而暴露数据并增加通信成本。为了解决这个问题，我们提出了一种分散优化算法，它在通信效率上高效，并通过最优梯度复杂性达到了一个 ε- 近似解，对于凸和强凸的设置分别为 O (1/√ε+σ²/ε²) 和 O (log (1/ε)+σ²/ε)，对于两种设置都具有 O (1/ε²) 的 LO (线性优化) 复杂性，给定方差 σ² 的随机梯度预言。与之前的工作相比，我们的框架放宽了最优解是可行集的严格内点的假设，并在使用随机梯度预言进行大规模训练时具有更广泛的适用性。我们还通过各种数值实验证明了我们算法的效率。

基于梯度滑动方法的少数据神经网络分布式随机约束优化

Stochastic Constrained Decentralized Optimization for Machine Learning  with Fewer Data Oracles: a Gradient Sliding Approach

Non-stationarity is a fundamental challenge in multi-agent reinforcement
learning (MARL), where agents update their behaviour as they learn. Many
theoretical advances in MARL avoid the challenge of non-stationarity by
coordinating the policy updates of agents in various ways, including
synchronizing times at which agents are allowed to revise their policies.
Synchronization enables analysis of many MARL algorithms via multi-timescale
methods, but such synchrony is infeasible in many decentralized applications.
In this paper, we study an asynchronous variant of the decentralized Q-learning
algorithm, a recent MARL algorithm for stochastic games. We provide sufficient
conditions under which the asynchronous algorithm drives play to equilibrium
with high probability. Our solution utilizes constant learning rates in the
Q-factor update, which we show to be critical for relaxing the synchrony
assumptions of earlier work. Our analysis also applies to asynchronous
generalizations of a number of other algorithms from the regret testing
tradition, whose performance is analyzed by multi-timescale methods that study
Markov chains obtained via policy update dynamics. This work extends the
applicability of the decentralized Q-learning algorithm and its relatives to
settings in which parameters are selected in an independent manner, and tames
non-stationarity without imposing the coordination assumptions of prior work.

这篇研究论文探讨了多智能体强化学习中的非静态挑战，介绍了一种异步变种的分散式 Q 学习算法，并提供了使异步算法以高概率驱动到均衡的充分条件。它还将该算法及其相关方法的适用性扩展到参数独立选择的环境，并在不强加协调假设的情况下驯服了非静态挑战。

异步分散式 Q-Learning: 持久性的两时间尺度分析

Asynchronous Decentralized Q-Learning: Two Timescale Analysis By  Persistence

The blockchain technology is believed by many to be a game changer in many
application domains, especially financial applications. While the first
generation of blockchain technology (i.e., Blockchain 1.0) is almost
exclusively used for cryptocurrency purposes, the second generation (i.e.,
Blockchain 2.0), as represented by Ethereum, is an open and decentralized
platform enabling a new paradigm of computing --- Decentralized Applications
(DApps) running on top of blockchains. The rich applications and semantics of
DApps inevitably introduce many security vulnerabilities, which have no
counterparts in pure cryptocurrency systems like Bitcoin. Since Ethereum is a
new, yet complex, system, it is imperative to have a systematic and
comprehensive understanding on its security from a holistic perspective, which
is unavailable. To the best of our knowledge, the present survey, which can
also be used as a tutorial, fills this void. In particular, we systematize
three aspects of Ethereum systems security: vulnerabilities, attacks, and
defenses. We draw insights into, among other things, vulnerability root causes,
attack consequences, and defense capabilities, which shed light on future
research directions.

介绍了以太坊的新一代区块链技术及其去中心化应用（DApps）的概念，分析了 DApps 系统中存在的安全漏洞、攻击和防御的能力，提出了未来的研究方向。