Rapport Année : 2025

FLAIR: Distributed Federated Learning with Dynamic Clustering

Résumé

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 leverages a probabilistic, verifiable cluster-head election mechanism, which is enhanced to favor nodes with greater computational and communication capabilities, thereby ensuring both fairness and efficiency. Through comprehensive simulations in ns-3, we evaluate FLAIR 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 resilience to mobility, 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.

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Dates et versions

hal-05289096 , version 1 (29-09-2025)

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  • HAL Id : hal-05289096 , version 1

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Ihssan Boutebicha, Bilel Zaghdoudi, Mohamed Amine Legheraba, Maria Potop Butucaru. FLAIR: Distributed Federated Learning with Dynamic Clustering. LIP6, Sorbonne Université, CNRS, UMR 7606. 2025. ⟨hal-05289096⟩
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