Search Results (3)

Traditional cattle health monitoring systems rely on centralized data collection, posing significant challenges related to data privacy, network connectivity, model reliability, and trust. This study introduces a novel, nature-inspired federated learning (FL) framework for cattle health monitoring, integrating blockchain to ensure model validation, system resilience, and reputation management. Inspired by the fission–fusion dynamics of elephant herds, the framework adaptively forms and merges subgroups of edge nodes based on six key parameters: health metrics, activity levels, geographical proximity, resource availability, temporal activity, and network connectivity. Graph attention networks (GATs) enable dynamic fission, while Density-Based Spatial Clustering of Applications with Noise (DBSCAN) supports subgroup fusion based on model similarity. Blockchain smart contracts validate model contributions and ensure that only high-performing models participate in global aggregation. A reputation-driven mechanism promotes reliable nodes and discourages unstable participants. Experimental results show the proposed framework achieves 94.3% model accuracy, faster convergence, and improved resource efficiency. This adaptive and privacy-preserving approach transforms cattle health monitoring into a more trustworthy, efficient, and resilient process. Full article

Clustered federated learning has garnered significant attention as an effective strategy for enhancing model performance in non-independent and identically distributed (non-IID) data scenarios. This approach improves model performance in such environments by calculating the similarity between users and clustering them into multiple groups. However, several challenges arise when implementing this method, particularly in balancing flexibility, communication costs, and model performance. To address these issues, this paper proposes a novel hierarchical federated learning framework that balances both network and model performance. The framework performs principal component analysis (PCA) on device-side image datasets to assess the similarity of private data across devices and, in conjunction with network performance measurements, dynamically adjusts communication strategies to minimize latency while ensuring stable model performance. By weighting similarity and communication metrics, the framework optimizes communication efficiency without significantly compromising model performance. To validate the proposed method’s effectiveness, we employed three publicly available datasets and compared it against four baseline methods. The experimental results demonstrate that SC-Fed (segmented clustering-federated learning) achieves a maximum accuracy improvement of 7.56% over baseline methods, while also reducing the average waiting time by 54.6%. These results indicate that the proposed algorithm significantly enhances the applicability and efficiency of clustered federated learning in practical training scenarios. Full article

Federated learning is becoming increasingly popular to enable automated learning in distributed networks of autonomous partners without sharing raw data. Many works focus on supervised learning, while the area of federated unsupervised learning, similar to federated clustering, is still less explored. In this paper, we introduce a federated clustering framework that solves three challenges: determine the number of global clusters in a federated dataset, obtain a partition of the data via a federated fuzzy c-means algorithm, and validate the clustering through a federated fuzzy Davies–Bouldin index. The complete framework is evaluated through numerical experiments on artificial and real-world datasets. The observed results are promising, as in most cases the federated clustering framework’s results are consistent with its nonfederated equivalent. Moreover, we embed an alternative federated fuzzy c-means formulation into our framework and observe that our formulation is more reliable in case the data are noni.i.d., while the performance is on par in the i.i.d. case. Full article