Active search refers to the problem of efficiently locating targets in an unknown environment by actively making data-collection decisions, and has many applications including detecting gas leaks, radiation sources or human survivors of disasters using aerial and/or ground robots (agents). Existing active search methods are in general only amenable to a single agent, or if they extend to multi agent they require a central control system to coordinate the actions of all agents. However, such control systems are often impractical in robotics applications. In this paper, we propose two distinct active search algorithms called SPATS (Sparse Parallel Asynchronous Thompson Sampling) and LATSI (LAplace Thompson Sampling with Information gain) that allow for multiple agents to independently make data-collection decisions without a central coordinator. Throughout we consider that targets are sparsely located around the environment in keeping with compressive sensing assumptions and its applicability in real world scenarios. Additionally, while most common search algorithms assume that agents can sense the entire environment (e.g. compressive sensing) or sense point-wise (e.g. Bayesian Optimization) at all times, we make a realistic assumption that each agent can only sense a contiguous region of space at a time. We provide simulation results as well as theoretical analysis to demonstrate the efficacy of our proposed algorithms.
AP-BMM: Approximating Capability-Cost Pareto Sets of LLMs via Asynchronous Prior-Guided Bayesian Model Merging
Guangdong Provincial Key Laboratory of Novel Security Intelligence Technologies（广东新型安全智能技术重点实验室）；Institute of Cyberspace Security（网络空间安全研究院）；School of Computer Science and Technology（计算机科学与技术学院）
Multi-fidelity Bayesian Optimization with Max-value Entropy Search and its parallelization
Gifu University（岐阜大学）；Nagoya Institute of Technology（名古屋工业大学）；Nagoya University（名古屋大学）；Japan Science and Technology Agency（日本科学技术振兴机构）；National Institute for Material Science（国立材料科学研究所）；RIKEN Center for Advanced Intelligence Project（理化学研究所先进智能项目中心）
Revisiting Multi-Agent Asynchronous Online Optimization with Delays: the Strongly Convex Case
Southeast University-Monash University Joint Graduate School（东南大学-墨尔本大学联合研究生院）；School of Computer Science and Engineering（计算机科学与工程学院）；School of Software Technology（软件技术学院）
Asynchronous Parallel Incremental Block-Coordinate Descent for Decentralized Machine Learning
Royal Institute of Technology (KTH)（瑞典皇家理工学院）
机器学习（Machine Learning，ML）是大数据驱动的大规模物联网（Internet of Things，IoT）智能普适计算建模与分析的关键技术。对于快速增长的应用程序和数据量，分布式学习是一个有前途的新兴范式，因为它通常是不切实际的或效率低下的共享/聚合数据到一个集中的位置从不同的。本文研究了在分散式系统上训练ML模型的问题，其中数据分布在许多用户设备上，学习算法在设备上运行，目的是减轻中央实体/服务器的负担。虽然基于流言的方法已经在不同的用例中用于此目的，但它们遭受高通信成本，特别是当设备数量很大时。为了缓解这一问题，提出了基于增量的方法。我们首先为去中心化ML引入增量块坐标下降（I-BCD），它可以以运行时间为代价降低通信成本。为了加快收敛速度，提出了一种异步并行增量BCD（API-BCD）方法，其中多个设备/代理以异步方式活动。我们推导出所提出的方法的收敛性。仿真结果还表明，我们的API-BCD方法在运行时间和通信成本方面优于现有技术。
Efficient Asynchronous Federated Learning with Sparsification and Quantization
School of Computer Science and Technology, Soochow University（苏州大学计算机科学与技术学院）；Hithink RoyalFlush Information Network Co., Ltd.（同花顺信息服务股份有限公司）；Collaborative Innovation Center of Novel Software Technology and Industrialization（新型软件技术及产业化协同创新中心）；Department of Sciences and Informatics, Muroran Institute of Technology（室兰工业大学科学与信息学系）；Boston Consulting Group（波士顿咨询公司）
