# FBFL: A Field-Based Coordination Approach for Data Heterogeneity in Federated Learning

arXiv: https://arxiv.org/abs/2502.08577 · HTML: https://mlsanity.com/paper/2502.08577/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- Builds on 24 of its 81 references that are in the map.
- First posted on arXiv 12 Feb 2025.

## Builds on (most cited first, 24 of 24)

- [Communication-Efficient Learning of Deep Networks from Decentralized Data](https://mlsanity.com/paper/1602.05629/) (2016)
- [Fashion-MNIST: a Novel Image Dataset for Benchmarking Machine Learning Algorithms](https://mlsanity.com/paper/1708.07747/) (2017)
- [Advances and Open Problems in Federated Learning](https://mlsanity.com/paper/1912.04977/) (2019)
- [Federated Optimization in Heterogeneous Networks](https://mlsanity.com/paper/1812.06127/) (2018)
- [Tackling the Objective Inconsistency Problem in Heterogeneous Federated Optimization](https://mlsanity.com/paper/2007.07481/) (2020)
- [Ensemble Distillation for Robust Model Fusion in Federated Learning](https://mlsanity.com/paper/2006.07242/) (2020)
- [Federated Learning on Non-IID Data Silos: An Experimental Study](https://mlsanity.com/paper/2102.02079/) (2021)
- [An Efficient Framework for Clustered Federated Learning](https://mlsanity.com/paper/2006.04088/) (2020)
- [Bayesian Nonparametric Federated Learning of Neural Networks](https://mlsanity.com/paper/1905.12022/) (2019)
- [EMNIST: an extension of MNIST to handwritten letters](https://mlsanity.com/paper/1702.05373/) (2017)
- [Client-Edge-Cloud Hierarchical Federated Learning](https://mlsanity.com/paper/1905.06641/) (2019)
- [Federated Learning for Smart Healthcare: A Survey](https://mlsanity.com/paper/2111.08834/) (2021)
- [Vertical Federated Learning: Concepts, Advances and Challenges](https://mlsanity.com/paper/2211.12814/) (2022)
- [Federated Learning for Generalization, Robustness, Fairness: A Survey and Benchmark](https://mlsanity.com/paper/2311.06750/) (2023)
- [Federated Learning with Cooperating Devices: A Consensus Approach for Massive IoT Networks](https://mlsanity.com/paper/1912.13163/) (2019)
- [Decentralized Collaborative Learning of Personalized Models over Networks](https://mlsanity.com/paper/1610.05202/) (2016)
- [Non-IID data in Federated Learning: A Survey with Taxonomy, Metrics, Methods, Frameworks and Future Directions](https://mlsanity.com/paper/2411.12377/) (2024)
- [A Higher-order Calculus of Computational Fields](https://mlsanity.com/paper/1610.08116/) (2016)
- [Engineering Resilient Collective Adaptive Systems by Self-Stabilisation](https://mlsanity.com/paper/1711.08297/) (2017)
- [Proximity-based Self-Federated Learning](https://mlsanity.com/paper/2407.12410/) (2024)
- [Asynchronous Distributed Execution Of Fixpoint-Based Computational Fields](https://mlsanity.com/paper/1610.00253/) (2016)
- [Field-based Coordination with the Share Operator](https://mlsanity.com/paper/1910.02874/) (2019)
- [Space-Fluid Adaptive Sampling by Self-Organisation](https://mlsanity.com/paper/2210.17505/) (2022)
- [MacroSwarm: A Field-based Compositional Framework for Swarm Programming](https://mlsanity.com/paper/2401.10969/) (2024)

## Built on by (most cited first, 5 of 5)

- [Sparse Self-Federated Learning for Energy Efficient Cooperative Intelligence in Society 5.0](https://mlsanity.com/paper/2507.07613/) (2025)
- [Phyelds: A Pythonic Framework for Aggregate Computing](https://mlsanity.com/paper/2603.29999/) (2026)
- [C2FL: Clustered Continual Federated Learning under Spatial and Temporal Drift](https://mlsanity.com/paper/2606.18003/) (2026)
- [A Typed Tensor Language for Shared-State Federated Computation](https://mlsanity.com/paper/2605.21103/) (2026)
- [Discovering Collaboration from Novelty: Random Network Distillation for Clustered Federated Learning](https://mlsanity.com/paper/2606.30499/) (2026)

## Abstract (arXiv)

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 using advanced macroprogramming patterns. Moreover, FBFL not only overcomes the aforementioned limitations, but also enables the development of more specialized models tailored to the specific data distribution in each subregion. This paper formalizes FBFL and evaluates it extensively using MNIST, FashionMNIST, and Extended MNIST datasets. We demonstrate that, when operating under IID data conditions, FBFL performs comparably to the widely-used FedAvg algorithm. Furthermore, in challenging non-IID scenarios, FBFL not only outperforms FedAvg but also surpasses other state-of-the-art methods, namely FedProx and Scaffold, which have been specifically designed to address non-IID data distributions. Additionally, we showcase the resilience of FBFL's self-organizing hierarchical architecture against server failures.