[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-82988-en":3,"doc-seo-82988-105":30,"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":21,"is_downloadable":21,"audit_status":21,"page_count":22,"language":23,"language_code":24,"site_id":25,"html_lang":24,"table_of_contents":26,"faqs":27,"seo_title":13,"seo_description":14,"update_tm":28,"read_time":29},82988,7971461740886,"Theodore","https://ap-avatar.wpscdn.com/davatar_3d24733baf745e90a7e4bdd5f77d97b2",8,"Research & Report","Inertia-Informed Federated Learning Control Framework for Distributed Smart Grid Resilience","Resilient-by-design smart grid control requires stable operation under physical disturbances and communication failures without centralized coordination. A centralized-training decentralized-execution (CTDE) learning paradigm at the grid edge faces limited generalization to unseen fault contingencies and challenges for fully decentralized deployment. Federated learning (FL) improves generalization through collaborative training, but standard aggregation ignores generator physical heterogeneity. IIWFedAvg embeds generator inertia into physics-informed weighted FedAvg for transient stability control, using ChebyKAN controllers with RoCoF features. On the IEEE 39-bus benchmark with full decentralization, IIWFedAvg reaches 75% success across unseen faults and speeds stabilization 3× without centralized overhead.","Inertia-Informed Federated Learning Control Framework for Distributed Smart Grid Resilience  \nIbrahim Shahbaz, Omar Al-Refai, Eman Hammad  \niSTAR Lab, Texas A&M University  \nCollege Station, TX, USA  \n{i.shahbaz, omaralrefai, [eman.hammad](eman.hammad}@tamu.edu)[}](eman.hammad}@tamu.edu)[@tamu.edu](eman.hammad}@tamu.edu)  \narXiv :2607 .05720v1 [ ee ss . SY] 7 Jul 2026  \nAbstract—Resilient-by-design smart grid control demands frameworks capable of maintaining stability under physical disturbances and communication failures, without reliance on centralized coordination. While Centralized Training Decentralized Execution (CTDE) enables a learning-based control paradigm at the grid edge, individually trained models fail to generalize across unseen fault contingencies and fall short of fully decentralized deployment. Federated learning (FL) restores generalization through collaborative training; however, standard aggregation strategies remain agnostic to the physical heterogeneity of synchronous generators. This work proposes InertiaInformed Weighted FedAvg (IIWFedAvg), a physics-informed aggregation strategy that embeds generator inertia directly into global model fusion for transient stability control in transmission networks. The proposed framework further integrates interpretable Chebyshev Kolmogorov-Arnold Network (ChebyKAN) -based controllers, augmented with Rate-of-Change-of-Frequency (RoCoF) features to enhance dynamic response awareness. Evaluated on the IEEE 39-bus benchmark under full decentralized deployment, IIWFedAvg achieves a 75% generalization success rate across unseen fault contingencies. It also surpasses the centralized baseline in two out of three stabilized faults, while delivering a 3× improvement in stabilization speed at zero centralized coordination overhead.  \nIndex Terms—Federated learning, physics-informed aggregation, Kolmogorov-Arnold networks, transient stability, smart grid resilience, distributed control.  \nI. INTRODUCTION  \nModern power systems are evolving into cognitive cyberphysical infrastructures with pervasive sensing, computation, communication, and control to support reliable operation and high penetration of distributed energy resources. In this context, resilience has emerged as a core design objective for smart grid (SG) control, requiring the system to maintain stable, reliable, and secure operation under a wide range of disturbances, uncertainties, and component failures [1], [2] . A key challenge is the trade-off between centralized and decentralized control schemes: while centralized approaches exploit global information for near-optimal performance, they are vulnerable to communication failures and single points of failure; conversely, decentralized approaches improve robustness and scalability but often sacrifice performance due to limited system-wide visibility [3] . This work addresses transient stability by proposing an inertia-informed centralized training and decentralized execution (CTDE) control framework that leverages federated learning (FL) to approximate centralized  \nclosed-loop actions under unseen fault contingencies in fully decentralized operation.  \nRecent advances in learning-based control, particularly reinforcement learning (RL) and FL, offer a promising pathway beyond the limitations of traditional control schemes [4] . RL synthesizes non-linear control policies directly from simulation interaction data, making it well-suited to handle complex grid dynamics [5], yet its online closed-loop deployment remains constrained by the need for extensive offline episodic training, while the weak interpretability of learned policies further hinders trust and adoption in safety-critical settings [6] . Complementarily, FL enables multiple agents to collaboratively train shared models without exchanging raw data, preserving privacy and reducing communication overhead [7] . The performance of FL-based control, however, is ultimately governed by the aggregation strategy that","cbCaidPiVeoZEE1i","https://ap.wps.com/l/cbCaidPiVeoZEE1i","pdf",3656573,2,1,7,"English","en",105,"# Introduction\n## Resilience and control trade-offs\n## Learning-based control via FL and aggregation","[{\"question\":\"What problem does the document address in smart grid control?\",\"answer\":\"It addresses transient stability and resilience under physical disturbances and communication failures while avoiding reliance on centralized coordination.\"},{\"question\":\"How does IIWFedAvg differ from standard federated aggregation?\",\"answer\":\"IIWFedAvg uses physics-informed weighting that embeds generator inertia into global model fusion to account for physical heterogeneity.\"},{\"question\":\"What controllers and features are integrated to improve dynamic response awareness?\",\"answer\":\"The framework integrates interpretable ChebyKAN-based controllers augmented with Rate-of-Change-of-Frequency (RoCoF) features.\"}]",1784184482,18,{"code":4,"msg":31,"data":32},"ok",{"site_id":25,"language":24,"slug":33,"title":13,"keywords":34,"description":14,"schema_data":35,"social_meta":86,"head_meta":88,"extra_data":90,"updated_unix":28},"inertia-informed-federated-learning-control-framework-for-distributed-smart-grid-resilience","",{"@graph":36,"@context":85},[37,53,68],{"@type":38,"itemListElement":39},"BreadcrumbList",[40,44,47,50],{"item":41,"name":42,"@type":43,"position":21},"https://docshare.wps.com","Home","ListItem",{"item":45,"name":46,"@type":43,"position":20},"https://docshare.wps.com/document/","Document",{"item":48,"name":12,"@type":43,"position":49},"https://docshare.wps.com/document/research-report/",3,{"item":51,"name":13,"@type":43,"position":52},"https://docshare.wps.com/document/inertia-informed-federated-learning-control-framework-for-distributed-smart-grid-resilience/82988/",4,{"url":51,"name":13,"@type":54,"author":55,"headline":13,"publisher":57,"fileFormat":60,"inLanguage":24,"description":14,"dateModified":61,"datePublished":62,"encodingFormat":60,"isAccessibleForFree":63,"interactionStatistic":64},"DigitalDocument",{"name":9,"@type":56},"Person",{"url":41,"name":58,"@type":59},"DocShare","Organization","application/pdf","2026-07-24","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 the document address in smart grid control?","Question",{"text":75,"@type":76},"It addresses transient stability and resilience under physical disturbances and communication failures while avoiding reliance on centralized coordination.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"How does IIWFedAvg differ from standard federated aggregation?",{"text":80,"@type":76},"IIWFedAvg uses physics-informed weighting that embeds generator inertia into global model fusion to account for physical heterogeneity.",{"name":82,"@type":73,"acceptedAnswer":83},"What controllers and features are integrated to improve dynamic response awareness?",{"text":84,"@type":76},"The framework integrates interpretable ChebyKAN-based controllers augmented with Rate-of-Change-of-Frequency (RoCoF) features.","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":25},{"code":4,"msg":5,"data":92},[93,97,101,105,110,115,119,122,127,130,134],{"id":21,"doc_module":4,"doc_module_name":46,"category_name":94,"show_sort_weight":95,"slug":96},"Story & Novel",90,"story-novel",{"id":20,"doc_module":4,"doc_module_name":46,"category_name":98,"show_sort_weight":99,"slug":100},"Literature",80,"literature",{"id":52,"doc_module":4,"doc_module_name":46,"category_name":102,"show_sort_weight":103,"slug":104},"Exam",70,"exam",{"id":106,"doc_module":4,"doc_module_name":46,"category_name":107,"show_sort_weight":108,"slug":109},5,"Comic",60,"comic",{"id":111,"doc_module":4,"doc_module_name":46,"category_name":112,"show_sort_weight":113,"slug":114},6,"Technology",50,"technology",{"id":22,"doc_module":4,"doc_module_name":46,"category_name":116,"show_sort_weight":117,"slug":118},"Healthcare",40,"healthcare",{"id":11,"doc_module":4,"doc_module_name":46,"category_name":12,"show_sort_weight":120,"slug":121},30,"research-report",{"id":123,"doc_module":4,"doc_module_name":46,"category_name":124,"show_sort_weight":125,"slug":126},9,"Religion & Spirituality",20,"religion-spirituality",{"id":125,"doc_module":4,"doc_module_name":46,"category_name":128,"show_sort_weight":125,"slug":129},"World Cup","world-cup",{"id":131,"doc_module":4,"doc_module_name":46,"category_name":132,"show_sort_weight":131,"slug":133},10,"Lifestyle","lifestyle",{"id":135,"doc_module":4,"doc_module_name":46,"category_name":136,"show_sort_weight":106,"slug":137},19,"General","general"]