[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-83033-en":3,"doc-seo-83033-105":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},83033,7971461740909,"Levi","https://ap-avatar.wpscdn.com/davatar_155a257f0dc6eb9ab79c44ca47cae57d",8,"Research & Report","FLAIR: Distributed Federated Learning with Dynamic Clustering","Federated Learning offers privacy-preserving distributed machine learning, yet centralized and hierarchical designs face scalability limits, resilience challenges, and single points of failure in dynamic, infrastructure-less environments such as sensor networks. FLAIR presents a fully decentralized federated learning protocol that combines dynamic, resource-aware secure self-organized clustering with in-cluster model training. A probabilistic verifiable cluster-head election promotes higher-capability nodes while maintaining fairness. Simulations in ns-3 across multiple scenarios show up to ~0.91 accuracy, graceful degradation above 0.85 under 90% failures, and under 2% loss with mobility.","arXiv :2607 .06025v 1 [ cs .NI ] 7 Jul 2026  \nFLAIR: Distributed Federated Learning with Dynamic Clustering  \nIhssan Boutebicha, Bilel Zaghdoudi, Mohamed Amine Legheraba, and Maria  \nPotop-Butucaru  \nLIP6, Sorbonne Université  \nParis, Île-de-France, F-75005, France  \n[Name.Surname@lip6.fr](Name.Surname@lip6.fr)  \nAbstract. Federated Learning (FL) offers a privacy-preserving framework for distributed machine learning, yet conventional centralized and hierarchical architectures present significant challenges in terms of scalability, resilience, and single points of failure, particularly in dynamic, infrastructure-less environments such as sensor networks. To address these limitations, we introduce FLAIR, a novel, fully decentralized FL protocol that integrates dynamic, resource-aware, secure, and self organized clustering with in-cluster model training. FLAIR uses a probabilistic, verifiable cluster-head election mechanism that is enhanced to promote nodes with greater computational and communication capabilities, ensuring both fairness and efficiency. Through comprehensive simulationsin ns-3, we evaluate our method against centralized, hierarchical, and gossip-based FL benchmarks across four demanding scenarios. The results demonstrate the superiority of our approach: in static 100-node networks, FLAIR achieves a final accuracy of approximately 0.91, outperforming all baselines. The protocol exhibits exceptional robustness, maintaining graceful degradation with accuracy above 0.85 even under 90% node failure rates. Furthermore, it shows strong mobility resilience, with a performance loss of less than 2% compared to static deployments. In a realistic smart farming simulation, FLAIR’s accuracy is within 0.2% of the centralized baseline, confirming its practical viability. These findings validate that FLAIR successfully combines the scalability of decentralized learning with the structural efficiency of clustering, presenting a robust and high-performing solution for large-scale, heterogeneous IoT systems.  \nKeywords: Federated Learning · Decentralized Learning · Clustering Algorithms · Resource-Aware · Network Resilience.  \n1 Introduction  \nFederated Learning (FL) has emerged as a powerful paradigm for privacypreserving machine learning on distributed data [11] . However, its conventional centralized architecture suffers from critical limitations, including scalability bottlenecks and single points of failure, which motivate decentralized  \n2 Boutebicha et al.  \napproaches [8, 18] . These challenges are especially pronounced in infrastructureless environments like wireless sensor networks (WSNs), where node resources and connectivity are dynamic.  \nIn parallel, clustering has been established as a key strategy in WSNs [19] such as those deployed for environmental monitoring, healthcare (e.g., body area networks), and smart cities, in order to enhance scalability and energy efficiency. Protocols like LEACH [4] pioneered the use of rotating cluster-heads to create an efficient, hierarchical network structure [4,3 ,21 , 12] . This approach provides a natural foundation for structuring distributed computation and communication.  \nCombining these two fields offers a promising path for robust decentralized learning. Using dynamic clusters for FL can reduce energy consumption, adapt to network heterogeneity, and mitigate node failures. Yet, despite this potential, many existing methods rely on fixed network structures or external infrastructure, which is inconsistent with the nature of sensor networks. For example, gossipbased aggregation [17], epidemic-style learning [2] or HEAL [10] still assume static overlays or require additional components, but do not fully integrate dynamic clustering. There remains a clear need for a protocol that unifies the principles of FL and dynamic clustering in a fully decentralized and adaptive manner.  \nTo bridge this critical gap, we introduce FLAIR (Federated Learning with Adaptive Integrity-preservin","cbCaitxeNakw9fGR","https://ap.wps.com/l/cbCaitxeNakw9fGR","pdf",3693525,1,14,"English","en",105,"# Introduction\n# Related Work","[{\"question\":\"What problem does FLAIR address in federated learning?\",\"answer\":\"FLAIR targets limitations of centralized and hierarchical FL, including scalability bottlenecks and single points of failure in dynamic, infrastructure-less settings like sensor networks.\"},{\"question\":\"How does FLAIR form clusters and select cluster-heads?\",\"answer\":\"FLAIR uses dynamic, resource-aware and secure self-organized clustering with a probabilistic, verifiable cluster-head election enhanced to favor nodes with stronger computational and communication capabilities.\"},{\"question\":\"What evidence is provided to validate FLAIR’s performance?\",\"answer\":\"Comprehensive ns-3 simulations compare FLAIR with centralized, hierarchical, and gossip-based FL across multiple demanding scenarios, including static networks, high node-failure rates, mobility, and a smart farming 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problem does FLAIR address in federated learning?","Question",{"text":75,"@type":76},"FLAIR targets limitations of centralized and hierarchical FL, including scalability bottlenecks and single points of failure in dynamic, infrastructure-less settings like sensor networks.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"How does FLAIR form clusters and select cluster-heads?",{"text":80,"@type":76},"FLAIR uses dynamic, resource-aware and secure self-organized clustering with a probabilistic, verifiable cluster-head election enhanced to favor nodes with stronger computational and communication capabilities.",{"name":82,"@type":73,"acceptedAnswer":83},"What evidence is provided to validate FLAIR’s performance?",{"text":84,"@type":76},"Comprehensive ns-3 simulations compare FLAIR with centralized, hierarchical, and gossip-based FL across multiple demanding scenarios, including static networks, high node-failure rates, mobility, and a smart farming 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