[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-85345-en":3,"doc-seo-85345-105":29,"detail-sidebar-cat-0-en-105":91},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":20,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":13,"seo_description":14,"update_tm":27,"read_time":28},85345,13056703020460,"Valentina","https://ap-avatar.wpscdn.com/avatar/be000253dac470eee5d?_k=1778207105932848923",8,"Research & Report","Privacy-Aware Collaborative and Distributed Bayesian Optimization","We propose a collaborative meta-learning framework for distributed Bayesian optimization that matches centralized performance without exchanging raw data. Gradient sharing is shown to leak client observations, with leakage increasing as the search converges and queries cluster near the optimum. A differentially private defense is evaluated, and a privacy–utility characterization is provided to guide practical deployment in privacy-constrained optimization systems.","Privacy-Aware Collaborative and Distributed  \nBayesian Optimization  \nAditya Rane* , Sathwik Yamana, Paritosh Ramanan, Srikanthan Ramesh, Akash Deep*  \nSchool of Industrial Engineering and Management  \nOklahoma State University, Stillwater, OK, USA  \nEmail: [aditya.rane10@okstate.edu](aditya.rane10@okstate.edu) ; [akash.deep@okstate.edu](akash.deep@okstate.edu)  \narXiv :2607 . 1 1600v 1 [ cs .LG] 13 Jul 2026  \nAbstract—We propose a collaborative meta-learning framework for distributed Bayesian optimization matching centralized performance without raw-data exchange. We show gradient sharing leaks client observations, with leakage worsening as the search converges and queries concentrate near the optimum. We evaluate a differentially private defense and characterize its privacy-utility trade-off.  \nIndex Terms—Collaborative Optimization, Meta-Learning, Differential Privacy, Manufacturing Process Optimization  \nI. INTRODUCTION  \nModern scientific and manufacturing systems are increasingly distributed, operating across multiple geographic locations, research laboratories, and distinct organizational entities. In applications such as optimizing drug formulations, tuning additive manufacturing parameters, refining semiconductor processes, and accelerating new material discovery, independent facilities or clients aim to optimize related processes under different operating conditions [1] [2] . In practice, manufacturers rely on repeated Design of Experiments (DOE) studies and iterative build-inspect-adjust loops to establish stable process windows for every new configuration [3] . These cycles significantly lengthen development timelines, increase material usage, and constrain the overall throughput of advanced manufacturing.  \nUnderlying these inefficiencies is that process knowledge remains isolated across manufacturing facilities. Because engineers at one facility cannot access the optimization data or insights generated by another, they are forced to \"start from scratch\" for each new objective, unable to benefit from the collective experience, exists across the broader manufacturing ecosystem. Overcoming this limitation requires collaborative learning methods capable of pooling distributed information to extract shared structural knowledge, an inductive bias across related processes. However, due to proprietary concerns, trade secrets, or regulatory constraints, sharing the raw process data required to build these joint models is prohibited [4] .  \nBayesian optimization (BO) has proven highly sampleefficient for optimizing expensive, black-box manufacturing processes by constructing probabilistic surrogate models to guide sequential decisions [5] . Standard BO frameworks, however, require raw data to be pooled in a single location, forcing facilities into either design isolation or mandatory data sharing. Recent advances in meta-learning, such as the PAC-Optimal Hyper-Posterior (PACOH) framework, enable extraction of a global inductive bias across related tasks but they  \nassume a single, centralized entity with access to all historical task data, again necessitating the sharing of confidential information [6] . Extending these meta-learning models to a collaborative, decentralized setting by exchanging task-specific knowledge in the form of gradients, rather than raw data, introduces severe and underexplored privacy risks. Specifically, communicating these gradients to a central coordinator exposes the system to adversarial exploitation, intercepted gradients can be leveraged in Deep Leakage from Gradients (DLG) attacks to reconstruct the proprietary training data, the distributed architecture was designed to protect [7] .  \nMotivated by this challenge, we present in this paper how Privacy-Aware Collaborative and Distributed Bayesian Optimization (PACD-BO), enables independent facilities to jointly learn a shared optimization prior without exchanging raw data. Extending PACOH meta-learning, PACD-BO distributes the meta-prior upda","cbCaipW9bDEQpSM8","https://ap.wps.com/l/cbCaipW9bDEQpSM8","pdf",622773,1,6,"English","en",105,"# Introduction\n# Related Work\n## Knowledge Transfer in Sequential Design\n## Collaborative and Distributed Optimization","[{\"question\":\"What problem does PACD-BO address in distributed Bayesian optimization?\",\"answer\":\"Facilities need to optimize related black-box manufacturing processes but cannot share raw process data. PACD-BO enables joint learning of a shared optimization prior while keeping observations local.\"},{\"question\":\"Why is gradient sharing risky in a collaborative Bayesian optimization setup?\",\"answer\":\"Communicating gradients can leak client observations. The document notes that leakage worsens as the optimization converges and queries concentrate near the optimum.\"},{\"question\":\"How does the paper defend against gradient-based privacy attacks?\",\"answer\":\"It evaluates a differentially private defense and analyzes the privacy–utility trade-off, treating differential privacy as a potential mitigation against leakage.\"}]",1784202667,15,{"code":4,"msg":30,"data":31},"ok",{"site_id":24,"language":23,"slug":32,"title":13,"keywords":33,"description":14,"schema_data":34,"social_meta":86,"head_meta":88,"extra_data":90,"updated_unix":27},"privacy-aware-collaborative-and-distributed-bayesian-optimization","",{"@graph":35,"@context":85},[36,53,68],{"@type":37,"itemListElement":38},"BreadcrumbList",[39,43,47,50],{"item":40,"name":41,"@type":42,"position":20},"https://docshare.wps.com","Home","ListItem",{"item":44,"name":45,"@type":42,"position":46},"https://docshare.wps.com/document/","Document",2,{"item":48,"name":12,"@type":42,"position":49},"https://docshare.wps.com/document/research-report/",3,{"item":51,"name":13,"@type":42,"position":52},"https://docshare.wps.com/document/privacy-aware-collaborative-and-distributed-bayesian-optimization/85345/",4,{"url":51,"name":13,"@type":54,"author":55,"headline":13,"publisher":57,"fileFormat":60,"inLanguage":23,"description":14,"dateModified":61,"datePublished":62,"encodingFormat":60,"isAccessibleForFree":63,"interactionStatistic":64},"DigitalDocument",{"name":9,"@type":56},"Person",{"url":40,"name":58,"@type":59},"DocShare","Organization","application/pdf","2026-07-17","2026-07-16",true,{"@type":65,"interactionType":66,"userInteractionCount":20},"InteractionCounter",{"@type":67},"ViewAction",{"@type":69,"mainEntity":70},"FAQPage",[71,77,81],{"name":72,"@type":73,"acceptedAnswer":74},"What problem does PACD-BO address in distributed Bayesian optimization?","Question",{"text":75,"@type":76},"Facilities need to optimize related black-box manufacturing processes but cannot share raw process data. PACD-BO enables joint learning of a shared optimization prior while keeping observations local.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"Why is gradient sharing risky in a collaborative Bayesian optimization setup?",{"text":80,"@type":76},"Communicating gradients can leak client observations. The document notes that leakage worsens as the optimization converges and queries concentrate near the optimum.",{"name":82,"@type":73,"acceptedAnswer":83},"How does the paper defend against gradient-based privacy attacks?",{"text":84,"@type":76},"It evaluates a differentially private defense and analyzes the privacy–utility trade-off, treating differential privacy as a potential mitigation against leakage.","https://schema.org",{"og:url":51,"og:type":87,"og:title":13,"og:site_name":58,"og:description":14},"article",{"robots":89,"canonical":51},"index,follow",{"doc_id":7,"site_id":24},{"code":4,"msg":5,"data":92},[93,97,101,105,110,114,119,122,127,130,134],{"id":20,"doc_module":4,"doc_module_name":45,"category_name":94,"show_sort_weight":95,"slug":96},"Story & Novel",90,"story-novel",{"id":46,"doc_module":4,"doc_module_name":45,"category_name":98,"show_sort_weight":99,"slug":100},"Literature",80,"literature",{"id":52,"doc_module":4,"doc_module_name":45,"category_name":102,"show_sort_weight":103,"slug":104},"Exam",70,"exam",{"id":106,"doc_module":4,"doc_module_name":45,"category_name":107,"show_sort_weight":108,"slug":109},5,"Comic",60,"comic",{"id":21,"doc_module":4,"doc_module_name":45,"category_name":111,"show_sort_weight":112,"slug":113},"Technology",50,"technology",{"id":115,"doc_module":4,"doc_module_name":45,"category_name":116,"show_sort_weight":117,"slug":118},7,"Healthcare",40,"healthcare",{"id":11,"doc_module":4,"doc_module_name":45,"category_name":12,"show_sort_weight":120,"slug":121},30,"research-report",{"id":123,"doc_module":4,"doc_module_name":45,"category_name":124,"show_sort_weight":125,"slug":126},9,"Religion & Spirituality",20,"religion-spirituality",{"id":125,"doc_module":4,"doc_module_name":45,"category_name":128,"show_sort_weight":125,"slug":129},"World Cup","world-cup",{"id":131,"doc_module":4,"doc_module_name":45,"category_name":132,"show_sort_weight":131,"slug":133},10,"Lifestyle","lifestyle",{"id":135,"doc_module":4,"doc_module_name":45,"category_name":136,"show_sort_weight":106,"slug":137},19,"General","general"]