摘要翻译：
描述了一种同时解决分布式数据集中多个学习任务的客户机-服务器体系结构。在这样的体系结构中，每个客户端与单个学习任务和相关的示例数据集相关联。该体系结构的目标是在保持单个数据隐私的同时，对多个数据集进行信息融合。服务器的作用是从客户端实时收集数据，并将信息编入公共数据库。该数据库中编码的信息可供所有客户端用于解决各自的学习任务，从而每个客户端都可以利用所有数据集的信息内容，而不必实际访问他人的私有数据。该算法框架基于正则化理论和核方法，使用了一类合适的混合效应核。通过一个模拟的音乐推荐系统对新方法进行了说明。
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英文标题：
《Client-server multi-task learning from distributed datasets》
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作者：
Francesco Dinuzzo, Gianluigi Pillonetto, Giuseppe De Nicolao
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最新提交年份：
2010
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分类信息：

一级分类：Computer Science        计算机科学
二级分类：Machine Learning        机器学习
分类描述：Papers on all aspects of machine learning research (supervised, unsupervised, reinforcement learning, bandit problems, and so on) including also robustness, explanation, fairness, and methodology. cs.LG is also an appropriate primary category for applications of machine learning methods.
关于机器学习研究的所有方面的论文（有监督的，无监督的，强化学习，强盗问题，等等），包括健壮性，解释性，公平性和方法论。对于机器学习方法的应用，CS.LG也是一个合适的主要类别。
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一级分类：Computer Science        计算机科学
二级分类：Artificial Intelligence        人工智能
分类描述：Covers all areas of AI except Vision, Robotics, Machine Learning, Multiagent Systems, and Computation and Language (Natural Language Processing), which have separate subject areas. In particular, includes Expert Systems, Theorem Proving (although this may overlap with Logic in Computer Science), Knowledge Representation, Planning, and Uncertainty in AI. Roughly includes material in ACM Subject Classes I.2.0, I.2.1, I.2.3, I.2.4, I.2.8, and I.2.11.
涵盖了人工智能的所有领域，除了视觉、机器人、机器学习、多智能体系统以及计算和语言（自然语言处理），这些领域有独立的学科领域。特别地，包括专家系统，定理证明（尽管这可能与计算机科学中的逻辑重叠），知识表示，规划，和人工智能中的不确定性。大致包括ACM学科类I.2.0、I.2.1、I.2.3、I.2.4、I.2.8和I.2.11中的材料。
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英文摘要：
  A client-server architecture to simultaneously solve multiple learning tasks from distributed datasets is described. In such architecture, each client is associated with an individual learning task and the associated dataset of examples. The goal of the architecture is to perform information fusion from multiple datasets while preserving privacy of individual data. The role of the server is to collect data in real-time from the clients and codify the information in a common database. The information coded in this database can be used by all the clients to solve their individual learning task, so that each client can exploit the informative content of all the datasets without actually having access to private data of others. The proposed algorithmic framework, based on regularization theory and kernel methods, uses a suitable class of mixed effect kernels. The new method is illustrated through a simulated music recommendation system.
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PDF链接：
https://arxiv.org/pdf/0812.4235