# Proximity-based Self-Federated Learning

arXiv: https://arxiv.org/abs/2407.12410 · HTML: https://mlsanity.com/paper/2407.12410/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- Builds on 21 of its 51 references that are in the map.
- First posted on arXiv 17 Jul 2024.

## Builds on (most cited first, 21 of 21)

- [Communication-Efficient Learning of Deep Networks from Decentralized Data](https://mlsanity.com/paper/1602.05629/) (2016)
- [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)
- [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)
- [Clustered Federated Learning: Model-Agnostic Distributed Multi-Task Optimization under Privacy Constraints](https://mlsanity.com/paper/1910.01991/) (2019)
- [Federated Learning on Non-IID Data: A Survey](https://mlsanity.com/paper/2106.06843/) (2021)
- [Federated Learning for Internet of Things: A Comprehensive Survey](https://mlsanity.com/paper/2104.07914/) (2021)
- [Client-Edge-Cloud Hierarchical Federated Learning](https://mlsanity.com/paper/1905.06641/) (2019)
- [SCAFFOLD: Stochastic Controlled Averaging for Federated Learning](https://mlsanity.com/paper/1910.06378/) (2019)
- [Flexible Clustered Federated Learning for Client-Level Data Distribution Shift](https://mlsanity.com/paper/2108.09749/) (2021)
- [Recent Advances on Federated Learning: A Systematic Survey](https://mlsanity.com/paper/2301.01299/) (2023)
- [Towards Effective Clustered Federated Learning: A Peer-to-peer Framework with Adaptive Neighbor Matching](https://mlsanity.com/paper/2203.12285/) (2022)
- [Self-organizing Democratized Learning: Towards Large-scale Distributed Learning Systems](https://mlsanity.com/paper/2007.03278/) (2020)
- [Macroprogramming: Concepts, State of the Art, and Opportunities of Macroscopic Behaviour Modelling](https://mlsanity.com/paper/2201.03473/) (2022)
- [Efficient Fully Distributed Federated Learning with Adaptive Local Links](https://mlsanity.com/paper/2203.12281/) (2022)
- [Adaptive Parameterization of Deep Learning Models for Federated Learning](https://mlsanity.com/paper/2302.02949/) (2023)
- [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)
- [Opportunistic Edge Computing: Concepts, Opportunities and Research Challenges](https://mlsanity.com/paper/1806.04311/) (2018)

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

- [FBFL: A Field-Based Coordination Approach for Data Heterogeneity in Federated Learning](https://mlsanity.com/paper/2502.08577/) (2025)
- [ProFed: a Benchmark for Proximity-based non-IID Federated Learning](https://mlsanity.com/paper/2503.20618/) (2025)
- [Sparse Self-Federated Learning for Energy Efficient Cooperative Intelligence in Society 5.0](https://mlsanity.com/paper/2507.07613/) (2025)
- [FedLAD: A Modular and Adaptive Testbed for Federated Log Anomaly Detection](https://mlsanity.com/paper/2512.08277/) (2025)
- [Trust-free Personalized Decentralized Learning](https://mlsanity.com/paper/2410.11378/) (2024)

## Abstract (arXiv)

In recent advancements in machine learning, federated learning allows a network of distributed clients to collaboratively develop a global model without needing to share their local data. This technique aims to safeguard privacy, countering the vulnerabilities of conventional centralized learning methods. Traditional federated learning approaches often rely on a central server to coordinate model training across clients, aiming to replicate the same model uniformly across all nodes. However, these methods overlook the significance of geographical and local data variances in vast networks, potentially affecting model effectiveness and applicability. Moreover, relying on a central server might become a bottleneck in large networks, such as the ones promoted by edge computing. Our paper introduces a novel, fully-distributed federated learning strategy called proximity-based self-federated learning that enables the self-organised creation of multiple federations of clients based on their geographic proximity and data distribution without exchanging raw data. Indeed, unlike traditional algorithms, our approach encourages clients to share and adjust their models with neighbouring nodes based on geographic proximity and model accuracy. This method not only addresses the limitations posed by diverse data distributions but also enhances the model's adaptability to different regional characteristics creating specialized models for each federation. We demonstrate the efficacy of our approach through simulations on well-known datasets, showcasing its effectiveness over the conventional centralized federated learning framework.