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.

Privacy-preserving integration of multiple institutional data for single-cell type identification with scPrivacy

The rapid accumulation of large-scale single-cell RNA-seq datasets from multiple institutions presents remarkable opportunities for automatically cell annotations through integrative analyses.However,the privacy issue has existed but being ignored,since we are limited to access and utilize all the reference datasets distributed in different institutions globally due to the prohibited data transmission across institutions by data regulation laws.To this end,we present scPrivacy,which is the first and generalized automatically single-cell type identification prototype to facilitate single cell annotations in a data privacy-preserving collaboration manner.We evaluated scPrivacy on a comprehensive set of publicly available benchmark datasets for single-cell type identification to stimulate the scenario that the reference datasets are rapidly generated and distributed in multiple institutions,while they are prohibited to be integrated directly or exposed to each other due to the data privacy regulations,demonstrating its effectiveness,time efficiency and robustness for privacy-preserving integration of multiple institutional datasets in single cell annotations.

Conservative Treatment of Fetal Goitrous Hypothyroidism Due to Thyroglobulin Mutations:A Case Report and Literature Review

With the advances in fetal medicine,there will be more cases of congenital hypothyroidism(CH)diagnosed in the fetal period.However,there is no consensus on the management protocol.We present a successful case of conservatively managed fetal goitrous hypothyroidism due to compound heterozygous TG mutations.Goiter was observed in a fetus at 23 weeks of gestation.Because there was no evidence of transplacental passage of antithyroid antibody and drugs,iodine overload,and iodine deficiency,the fetus was highly suspected to have CH.Considering the potential risks of amniocentesis/cordocentesis,and lack of available parenteral levothyroxine in China,the fetus was closely monitored thereafter.A male neonate was delivered vaginally without complications at 39 weeks of gestation.We verified severe hypothyroidism in the infant and immediately initiated levothyroxine therapy.His growth and mental development were normal at the age of 8 month.Whole-exome sequencing showed that the neonate had two compound heterozygous mutations in the TG gene.We also performed a literature review of the prognosis of postnatal treatment of CH due to TG mutations and the result showed that postnatal treatment of CH due to TG mutations has a favorable prognosis.However,further prospective studies are warranted to verify this conclusion.