Enhancing Federated Learning Through Dynamic Approaches and Real-World Applications

The recent advancements in federated learning (FL) have significantly enhanced the efficiency and privacy of decentralized model training across various domains. A notable trend is the development of semi-supervised and dynamic approaches to address the non-Independent and Identically Distributed (non-IID) data challenges prevalent in FL. These methods leverage novel clustering and grouping techniques, as well as advanced aggregation algorithms, to ensure data IIDness and improve model performance. Additionally, the integration of FL with UAV swarms and IoT networks has shown promising results in real-world applications, particularly in environmental monitoring and intrusion detection. The introduction of entropy-driven participant selection and dynamic data queues has further optimized convergence rates and accuracy in FL systems. Notably, the use of MMD-based early stopping in adaptive GNSS interference classification demonstrates a significant advancement in handling out-of-distribution data effectively. These innovations collectively push the boundaries of FL applicability and robustness, making it a versatile tool for privacy-preserving and resource-efficient machine learning in diverse settings.

Sources

FedMSE: Federated learning for IoT network intrusion detection

DPVS-Shapley:Faster and Universal Contribution Evaluation Component in Federated Learning

Geographical Node Clustering and Grouping to Guarantee Data IIDness in Federated Learning

Federated Learning with MMD-based Early Stopping for Adaptive GNSS Interference Classification

Distributed Learning for UAV Swarms

Enhancing Federated Learning Convergence with Dynamic Data Queue and Data Entropy-driven Participant Selection

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