# Adaptive Configuration for Heterogeneous Participants in Decentralized Federated Learning

arXiv: https://arxiv.org/abs/2212.02136 · HTML: https://mlsanity.com/paper/2212.02136/

## Key facts

- Cited by 11 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 5 Dec 2022.

## Built on by (most cited first, 11 of 11)

- [SatFed: A Resource-Efficient LEO Satellite-Assisted Heterogeneous Federated Learning Framework](https://mlsanity.com/paper/2409.13503/) (2024)
- [MergeSFL: Split Federated Learning with Feature Merging and Batch Size Regulation](https://mlsanity.com/paper/2311.13348/) (2023)
- [Federated Learning While Providing Model as a Service: Joint Training and Inference Optimization](https://mlsanity.com/paper/2312.12863/) (2023)
- [Fair Concurrent Training of Multiple Models in Federated Learning](https://mlsanity.com/paper/2404.13841/) (2024)
- [Does Worst-Performing Agent Lead the Pack? Analyzing Agent Dynamics in Unified Distributed SGD](https://mlsanity.com/paper/2409.17499/) (2024)
- [A Semi-Supervised Federated Learning Framework with Hierarchical Clustering Aggregation for Heterogeneous Satellite Networks](https://mlsanity.com/paper/2507.22339/) (2025)
- [SemiSFL: Split Federated Learning on Unlabeled and Non-IID Data](https://mlsanity.com/paper/2307.15870/) (2023)
- [Secure Decentralized Learning with Blockchain](https://mlsanity.com/paper/2310.07079/) (2023)
- [Heterogeneity-Aware Resource Allocation and Topology Design for Hierarchical Federated Edge Learning](https://mlsanity.com/paper/2409.19509/) (2024)
- [How to Evaluate Participant Contributions in Decentralized Federated Learning](https://mlsanity.com/paper/2505.23246/) (2025)
- [Asynchronous Decentralized Federated Learning over Lossy Wireless Links via Reception- and Age-Aware Aggregation](https://mlsanity.com/paper/2606.10774/) (2026)

## Abstract (arXiv)

Data generated at the network edge can be processed locally by leveraging the paradigm of edge computing (EC). Aided by EC, decentralized federated learning (DFL), which overcomes the single-point-of-failure problem in the parameter server (PS) based federated learning, is becoming a practical and popular approach for machine learning over distributed data. However, DFL faces two critical challenges, ie, system heterogeneity and statistical heterogeneity introduced by edge devices. To ensure fast convergence with the existence of slow edge devices, we present an efficient DFL method, termed FedHP, which integrates adaptive control of both local updating frequency and network topology to better support the heterogeneous participants. We establish a theoretical relationship between local updating frequency and network topology regarding model training performance and obtain a convergence upper bound. Upon this, we propose an optimization algorithm, that adaptively determines local updating frequencies and constructs the network topology, so as to speed up convergence and improve the model accuracy. Evaluation results show that the proposed FedHP can reduce the completion time by about 51% and improve model accuracy by at least 5% in heterogeneous scenarios, compared with the baselines.