[2502.08577] FBFL: A Field-Based Coordination Approach for Data Heterogeneity in Federated Learning

Computer Science > Machine Learning arXiv:2502.08577 (cs) [Submitted on 12 Feb 2025 (v1), last revised 5 Mar 2026 (this version, v5)] Title:FBFL: A Field-Based Coordination Approach for Data Heterogeneity in Federated Learning Authors:Davide Domini, Gianluca Aguzzi, Lukas Esterle, Mirko Viroli View a PDF of the paper titled FBFL: A Field-Based Coordination Approach for Data Heterogeneity in Federated Learning, by Davide Domini and 3 other authors View PDF Abstract:In the last years, Federated learning (FL) has become a popular solution to train machine learning models in domains with high privacy concerns. However, FL scalability and performance face significant challenges in real-world deployments where data across devices are non-independently and identically distributed (non-IID). The heterogeneity in data distribution frequently arises from spatial distribution of devices, leading to degraded model performance in the absence of proper handling. Additionally, FL typical reliance on centralized architectures introduces bottlenecks and single-point-of-failure risks, particularly problematic at scale or in dynamic environments. To close this gap, we propose Field-Based Federated Learning (FBFL), a novel approach leveraging macroprogramming and field coordination to address these limitations through: (i) distributed spatial-based leader election for personalization to mitigate non-IID data challenges; and (ii) construction of a self-organizing, hierarchical architecture usi...