Decentralized proximity-aware clustering for collective self-federated learning

In recent years, Federated Learning (FL) has emerged as a privacy-preserving paradigm for collaborative model training in IoT systems, enabling clients to learn a global model for tasks like classification, prediction, or anomaly detection in IoT environments without sharing raw data. However, traditional centralized FL architectures face bottlenecks, single points of failure, and struggle with non-IID data. These limitations hinder effective Collective Intelligence in large-scale IoT systems where numerous devices operate across diverse and dynamic environments. Existing clustered FL approaches often retain centralization or overlook how the spatial distribution inherent in IoT deployments directly influences data heterogeneity, challenging both the integration of spatially correlated devices and the establishment of intelligence distributed across the entire system. Creating such intelligence demands both decentralized architectures for scalability and effective integration of devices with similar data distributions. For these reasons, this article introduces Proximity-Aware Self-Federated Learning (PSFL), a novel decentralized approach embodying collective intelligence principles. PSFL leverages field-based coordination to enable IoT devices to form self-federations, dynamically clustered groups that train specialized models based on both spatial proximity and local model characteristics. These self-federations reflect underlying data distributions, creating a distributed ecosystem of specialized models across the network. This approach overcomes global model limitations in non-IID settings through specialized federations based on local data distributions, enhancing performance while maintaining decentralization. We evaluate our approach using the Extended MNIST and CIFAR-100 datasets against state-of-the-art baselines, demonstrating its effectiveness in forming coherent, localized models under non-IID conditions.