基于属性异构网络表示学习的专利交易推荐

融合异构信息进行专利交易推荐可以促进交易,但存在因忽略专利属性而影响推荐结果的问题。本研究提出基于属性异构网络(attribute heterogeneous network,AHN)表示学习的专利交易推荐模型(patent transaction recommendation based on AHN representation learning,AHNRL-PTR)。首先筛选专利和组织中影响专利交易的属性;其次构建专利交易AHN,然后在AHN中引入网络表示学习,并基于多维高斯分布解决节点表示的不确定性,基于KL散度(Kullback-Leibler divergence)解决节点间距离非对称性。最后,以粤港澳大湾区有效发明授权专利数据进行实证研究,得出结论:第一,相比于metapath2vec、TADW(text-associated DeepWalk)和AHNRL-PTR模型的两个变体方法,AHNRL-PTR模型的推荐精度最高,超过86%,说明融合组织及专利属性,并聚焦节点表示的不确定性和非对称性问题的解决,能大幅提高推荐精度;第二,在非准确指标IntraSim和Popularity上,AHNRL-PTR的表现优于metapath2vec和两个变体方法,反映该方法的推荐结果具有一定的多样性,且可以挖掘推荐冷门专利;第三,基于两个非准确指标将组织聚类为六类,分别为中介型、领域骨干型、研究型、族群型、成长型、专业型,体现了推荐结果的可解释性和个性化水平。本研究可为专利交易智能化推荐服务提供决策支持。

Federated Learning with Privacy-preserving and Model IP-right-protection

In the past decades,artificial intelligence(AI)has achieved unprecedented success,where statistical models become the central entity in AI.However,the centralized training and inference paradigm for building and using these models is facing more and more privacy and legal challenges.To bridge the gap between data privacy and the need for data fusion,an emerging AI paradigm feder-ated learning(FL)has emerged as an approach for solving data silos and data privacy problems.Based on secure distributed AI,feder-ated learning emphasizes data security throughout the lifecycle,which includes the following steps:data preprocessing,training,evalu-ation,and deployments.FL keeps data security by using methods,such as secure multi-party computation(MPC),differential privacy,and hardware solutions,to build and use distributed multiple-party machine-learning systems and statistical models over different data sources.Besides data privacy concerns,we argue that the concept of“model”matters,when developing and deploying federated models,they are easy to expose to various kinds of risks including plagiarism,illegal copy,and misuse.To address these issues,we introduce FedIPR,a novel ownership verification scheme,by embedding watermarks into FL models to verify the ownership of FL models and protect model intellectual property rights(IPR or IP-right for short).While security is at the core of FL,there are still many articles re-ferred to distributed machine learning with no security guarantee as“federated learning”,which are not satisfied with the FL definition supposed to be.To this end,in this paper,we reiterate the concept of federated learning and propose secure federated learning(SFL),where the ultimate goal is to build trustworthy and safe AI with strong privacy-preserving and IP-right-preserving.We provide a com-prehensive overview of existing works,including threats,attacks,and defenses in each phase of SFL from the lifecycle perspective.