# Coordination-free Decentralised Federated Learning on Complex Networks: Overcoming Heterogeneity

arXiv: https://arxiv.org/abs/2312.04504 · HTML: https://mlsanity.com/paper/2312.04504/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- Builds on 20 of its 31 references that are in the map.
- First posted on arXiv 7 Dec 2023.

## Builds on (most cited first, 20 of 20)

- [Distilling the Knowledge in a Neural Network](https://mlsanity.com/paper/1503.02531/) (2015)
- [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)
- [On the Convergence of FedAvg on Non-IID Data](https://mlsanity.com/paper/1907.02189/) (2019)
- [Can Decentralized Algorithms Outperform Centralized Algorithms? A Case Study for Decentralized Parallel Stochastic Gradient Descent](https://mlsanity.com/paper/1705.09056/) (2017)
- [Client Selection for Federated Learning with Heterogeneous Resources in Mobile Edge](https://mlsanity.com/paper/1804.08333/) (2018)
- [A Field Guide to Federated Optimization](https://mlsanity.com/paper/2107.06917/) (2021)
- [Heterogeneous Federated Learning: State-of-the-art and Research Challenges](https://mlsanity.com/paper/2307.10616/) (2023)
- [On the Convergence of Decentralized Gradient Descent](https://mlsanity.com/paper/1310.7063/) (2013)
- [Asynchronous Decentralized Parallel Stochastic Gradient Descent](https://mlsanity.com/paper/1710.06952/) (2017)
- [Achieving Geometric Convergence for Distributed Optimization over Time-Varying Graphs](https://mlsanity.com/paper/1607.03218/) (2016)
- [Decentralized Federated Averaging](https://mlsanity.com/paper/2104.11375/) (2021)
- [Federated Learning with Cooperating Devices: A Consensus Approach for Massive IoT Networks](https://mlsanity.com/paper/1912.13163/) (2019)
- [On Nonconvex Decentralized Gradient Descent](https://mlsanity.com/paper/1608.05766/) (2016)
- [Federated Learning with Taskonomy for Non-IID Data](https://mlsanity.com/paper/2103.15947/) (2021)
- [Decentralized and Model-Free Federated Learning: Consensus-Based Distillation in Function Space](https://mlsanity.com/paper/2104.00352/) (2021)
- [Decentralized Bayesian Learning over Graphs](https://mlsanity.com/paper/1905.10466/) (2019)
- [A Framework for Energy and Carbon Footprint Analysis of Distributed and Federated Edge Learning](https://mlsanity.com/paper/2103.10346/) (2021)
- [The effect of network topologies on fully decentralized learning: a preliminary investigation](https://mlsanity.com/paper/2307.15947/) (2023)
- [Exploring the Impact of Disrupted Peer-to-Peer Communications on Fully Decentralized Learning in Disaster Scenarios](https://mlsanity.com/paper/2310.02986/) (2023)

## Built on by (most cited first, 6 of 6)

- [Impact of network topology on the performance of Decentralized Federated Learning](https://mlsanity.com/paper/2402.18606/) (2024)
- [Robustness of Decentralised Learning to Nodes and Data Disruption](https://mlsanity.com/paper/2405.02377/) (2024)
- [Initialisation and Network Effects in Decentralised Federated Learning](https://mlsanity.com/paper/2403.15855/) (2024)
- [The Built-In Robustness of Decentralized Federated Averaging to Bad Data](https://mlsanity.com/paper/2502.18097/) (2025)
- [DecHW: Heterogeneous Decentralized Federated Learning Exploiting Second-Order Information](https://mlsanity.com/paper/2601.19938/) (2026)
- [Decentralised Federated Learning over Temporal Networks: The Role of Heterogeneities](https://mlsanity.com/paper/2607.03171/) (2026)

## Abstract (arXiv)

Federated Learning (FL) is a well-known framework for successfully performing a learning task in an edge computing scenario where the devices involved have limited resources and incomplete data representation. The basic assumption of FL is that the devices communicate directly or indirectly with a parameter server that centrally coordinates the whole process, overcoming several challenges associated with it. However, in highly pervasive edge scenarios, the presence of a central controller that oversees the process cannot always be guaranteed, and the interactions (i.e., the connectivity graph) between devices might not be predetermined, resulting in a complex network structure. Moreover, the heterogeneity of data and devices further complicates the learning process. This poses new challenges from a learning standpoint that we address by proposing a communication-efficient Decentralised Federated Learning (DFL) algorithm able to cope with them. Our solution allows devices communicating only with their direct neighbours to train an accurate model, overcoming the heterogeneity induced by data and different training histories. Our results show that the resulting local models generalise better than those trained with competing approaches, and do so in a more communication-efficient way.