[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-124651-en":3,"doc-seo-124651-105":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":4,"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":27,"seo_description":14,"update_tm":28,"read_time":29},124651,549758146520,"Patrick","https://ap-avatar.wpscdn.com/avatar/80002397d8c0411e94?_k=1775819394049821470",8,"Research & Report","Decentralized Machine Learning Based Energy Efficient Routing and Intrusion Detection in Unmanned Aerial Network - Secure architecture proposal","Decentralized machine learning via federated learning (FL) enables multiple clients to collaboratively solve distributed machine learning tasks without exposing locally stored sensitive data. The study targets next-generation UAV-enabled wireless networks where centralized FL aggregation and unreliable links can cause single points of failure, packet loss, and energy imbalance. A security-focused decentralized approach uses peer-to-peer trust policies, preshared UAV lists and asymmetric encryption to detect hijacking and wrong information. Routing is secured with SLIDS and energy-efficient link-break prediction via LEER, achieving strong overhead, delay, energy efficiency, PDR, and throughput.","Decentralized Machine Learning based Energy Efficient Routing and Intrusion Detection in Unmanned Aerial Network (UAV)  \nC.Srinivas1,* ,S.Venkatramulu2,V.Chandra Shekar Rao3, ,B.Raghuram4, K.VinayKumar5 and Sreenivas Pratapagiri6  \n1,2,3,4,5&6Department of Computer Science and Engineering, Kakatiya Institute of Technology and Science, Warangal.  \n* Corresponding Author: [cs.cse@kitsw.ac.in](cs.cse@kitsw.ac.in)  \nAbstract  \nDecentralized machine learning (FL) is a system that uses federated learning (FL) . Without disclosing locally stored sensitive information, FL enables multiple clients to work together to solve conventional distributed ML problems coordinated by a central server. In order to classify FLs, this research relies heavily on machine learning and deep learning techniques. The next generation of wireless networks is anticipated to incorporate unmanned aerial vehicles (UAVs) like drones into both civilian and military applications. The use of artificial intelligence (AI), and more specifically machine learning (ML) methods, to enhance the intelligence of UAV networks is desirable and necessary for the aforementioned uses. Unfortunately, most existing FL paradigms are still centralized, with a singular entity accountable for network-wide ML model aggregation and fusion. This is inappropriate for UAV networks, which frequently feature unreliable nodes and connections, and provides a possible single point of failure. There are many challenges by using high mobility of UAVs, of loss of packet frequent and difficulties in the UAV between the weak links, which affect the reliability while delivering data. An earlier UAV failure is happened by the unbalanced conception of energy and lifetime of the network is decreased; this will accelerate consequently in the overall network. In this paper, we focused mainly on the technique of security while maintaining UAV network in surveillance context, all information collected from different kinds of sources. The trust policies are based on peer-to-peer information which is confirmed by UAV network. A preshared UAV list or used by asymmetric encryption security in the proposal system. The wrong information can be identified when the UAV the network is hijacked physically by using this proposed technique. To provide secure routing path by using Secure Location with Intrusion Detection System (SLIDS) and conservation of energy-based prediction of link breakage done by location-based energy efficient routing (LEER) for discovering path of degree connectivity. Thus, the proposed novel architecture is named as Decentralized Federate Learning- Secure Location with Intrusion Detection System (DFL-SLIDS), which achieves 98% of routing overhead, 93% of end-to-end delay, 92% of energy efficiency, 86.4% of PDR and 97% of throughput.  \nKeywords-federated learning, machine learning, intrusion, energy efficiency, Unmanned aerial vehicles (UAVs) .  \nI. Introduction  \nMajor changes are occurring in the next iteration of wireless networks. By 2025, Cisco predicts, there will be more than 75 billion Internet of Things (IoT) devices [1], including sensors, wearables, smartphones, linked automobiles, and UAVs. This shift is fueling an explosion in the quantity of wireless data being transmitted between the many different kinds of linked devices. UAVs, also known as drones, are expanding rapidly in this setting due to their many useful uses in wireless networks [2] [3] . They range from military and telecommunications to hospital supply delivery and surveillance and monitoring. Due to their unique qualities, UAVs in particular can serve as providers of wireless network infrastructure, improving the capacity, coverage, and energy economy of these networks. Remote sensing, augmented reality, and package transportation are  \njust some of the uses for UAVs, which can also function as aerial users of the current wireless infrastructure. In reality, the requirements and needs of these new applications are sh","cbCaiebOqAaIq0mN","https://ap.wps.com/l/cbCaiebOqAaIq0mN","pdf",636367,1,11,"English","en",105,"# Introduction\n## Federated learning and decentralization\n## UAV network requirements and challenges\n# Proposed architecture and security design\n## Peer-to-peer trust and encryption\n## SLIDS and intrusion detection\n## LEER and energy-aware routing\n# Performance results\n## Routing overhead, delay, energy, PDR, throughput","[{\"question\":\"What problem does decentralized federated learning address in UAV networks?\",\"answer\":\"It avoids centralized aggregation that can create a single point of failure when UAV nodes and connections are unreliable.\"},{\"question\":\"How does the proposed system detect wrong or malicious information?\",\"answer\":\"It applies peer-to-peer trust policies confirmed by UAV networking, using a preshared UAV list and asymmetric encryption to identify incorrect information after hijacking.\"},{\"question\":\"How are secure routing paths and energy efficiency achieved?\",\"answer\":\"Secure Location with Intrusion Detection System (SLIDS) supports intrusion-aware routing, while location-based energy efficient routing (LEER) predicts link breakage using energy conservation for path discovery.\"}]","Decentralized Machine Learning Based Energy Efficient Routing and Intrusion Detection in Unmanned Aerial Network - Secure architecture proposal | PDF",1785893529,28,{"code":4,"msg":31,"data":32},"ok",{"site_id":24,"language":23,"slug":33,"title":13,"keywords":34,"description":14,"schema_data":35,"social_meta":86,"head_meta":88,"extra_data":90,"updated_unix":28},"decentralized-machine-learning-based-energy-efficient-routing-and-intrusion-detection-in-unmanned-aerial-network-secure-architecture-proposal","",{"@graph":36,"@context":85},[37,54,68],{"@type":38,"itemListElement":39},"BreadcrumbList",[40,44,48,51],{"item":41,"name":42,"@type":43,"position":20},"https://docshare.wps.com","Home","ListItem",{"item":45,"name":46,"@type":43,"position":47},"https://docshare.wps.com/document/","Document",2,{"item":49,"name":12,"@type":43,"position":50},"https://docshare.wps.com/document/research-report/",3,{"item":52,"name":13,"@type":43,"position":53},"https://docshare.wps.com/document/decentralized-machine-learning-based-energy-efficient-routing-and-intrusion-detection-in-unmanned-aerial-network-secure-architecture-proposal/124651/",4,{"url":52,"name":13,"@type":55,"author":56,"headline":13,"publisher":58,"fileFormat":61,"inLanguage":23,"description":14,"dateModified":62,"datePublished":62,"encodingFormat":61,"isAccessibleForFree":63,"interactionStatistic":64},"DigitalDocument",{"name":9,"@type":57},"Person",{"url":41,"name":59,"@type":60},"DocShare","Organization","application/pdf","2026-08-05",true,{"@type":65,"interactionType":66,"userInteractionCount":4},"InteractionCounter",{"@type":67},"ViewAction",{"@type":69,"mainEntity":70},"FAQPage",[71,77,81],{"name":72,"@type":73,"acceptedAnswer":74},"What problem does decentralized federated learning address in UAV networks?","Question",{"text":75,"@type":76},"It avoids centralized aggregation that can create a single point of failure when UAV nodes and connections are unreliable.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"How does the proposed system detect wrong or malicious information?",{"text":80,"@type":76},"It applies peer-to-peer trust policies confirmed by UAV networking, using a preshared UAV list and asymmetric encryption to identify incorrect information after hijacking.",{"name":82,"@type":73,"acceptedAnswer":83},"How are secure routing paths and energy efficiency achieved?",{"text":84,"@type":76},"Secure Location with Intrusion Detection System (SLIDS) supports intrusion-aware routing, while location-based energy efficient routing (LEER) predicts link breakage using energy conservation for path discovery.","https://schema.org",{"og:url":52,"og:type":87,"og:title":13,"og:site_name":59,"og:description":14},"article",{"robots":89,"canonical":52},"index,follow",{"doc_id":7,"site_id":24},{"code":4,"msg":5,"data":92},[93,97,101,105,110,115,120,123,128,131,135],{"id":20,"doc_module":4,"doc_module_name":46,"category_name":94,"show_sort_weight":95,"slug":96},"Story & Novel",90,"story-novel",{"id":47,"doc_module":4,"doc_module_name":46,"category_name":98,"show_sort_weight":99,"slug":100},"Literature",80,"literature",{"id":53,"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":116,"doc_module":4,"doc_module_name":46,"category_name":117,"show_sort_weight":118,"slug":119},7,"Healthcare",40,"healthcare",{"id":11,"doc_module":4,"doc_module_name":46,"category_name":12,"show_sort_weight":121,"slug":122},30,"research-report",{"id":124,"doc_module":4,"doc_module_name":46,"category_name":125,"show_sort_weight":126,"slug":127},9,"Religion & Spirituality",20,"religion-spirituality",{"id":126,"doc_module":4,"doc_module_name":46,"category_name":129,"show_sort_weight":126,"slug":130},"World Cup","world-cup",{"id":132,"doc_module":4,"doc_module_name":46,"category_name":133,"show_sort_weight":132,"slug":134},10,"Lifestyle","lifestyle",{"id":136,"doc_module":4,"doc_module_name":46,"category_name":137,"show_sort_weight":106,"slug":138},19,"General","general"]