# Analysis and Evaluation of Synchronous and Asynchronous FLchain

arXiv: https://arxiv.org/abs/2112.07938 · HTML: https://mlsanity.com/paper/2112.07938/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 30 of its 69 references that are in the map.
- First posted on arXiv 15 Dec 2021.

## Builds on (most cited first, 30 of 30)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [Very Deep Convolutional Networks for Large-Scale Image Recognition](https://mlsanity.com/paper/1409.1556/) (2014)
- [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)
- [Federated Learning: Challenges, Methods, and Future Directions](https://mlsanity.com/paper/1908.07873/) (2019)
- [Federated Learning: Strategies for Improving Communication Efficiency](https://mlsanity.com/paper/1610.05492/) (2016)
- [Adaptive Federated Optimization](https://mlsanity.com/paper/2003.00295/) (2020)
- [Local Model Poisoning Attacks to Byzantine-Robust Federated Learning](https://mlsanity.com/paper/1911.11815/) (2019)
- [Adaptive Federated Learning in Resource Constrained Edge Computing Systems](https://mlsanity.com/paper/1804.05271/) (2018)
- [Federated Learning in Mobile Edge Networks: A Comprehensive Survey](https://mlsanity.com/paper/1909.11875/) (2019)
- [Asynchronous Federated Optimization](https://mlsanity.com/paper/1903.03934/) (2019)
- [FedHealth: A Federated Transfer Learning Framework for Wearable Healthcare](https://mlsanity.com/paper/1907.09173/) (2019)
- [Asynchronous Decentralized Parallel Stochastic Gradient Descent](https://mlsanity.com/paper/1710.06952/) (2017)
- [Federated Learning for Wireless Communications: Motivation, Opportunities and Challenges](https://mlsanity.com/paper/1908.06847/) (2019)
- [Blockchained On-Device Federated Learning](https://mlsanity.com/paper/1808.03949/) (2018)
- [Asynchronous Federated Learning on Heterogeneous Devices: A Survey](https://mlsanity.com/paper/2109.04269/) (2021)
- [Convergence Time Optimization for Federated Learning over Wireless Networks](https://mlsanity.com/paper/2001.07845/) (2020)
- [Federated Learning Meets Blockchain in Edge Computing: Opportunities and Challenges](https://mlsanity.com/paper/2104.01776/) (2021)
- [Incentive Mechanisms for Federated Learning: From Economic and Game Theoretic Perspective](https://mlsanity.com/paper/2111.11850/) (2021)
- [An Asynchronous Mini-Batch Algorithm for Regularized Stochastic Optimization](https://mlsanity.com/paper/1505.04824/) (2015)
- [A Secure Federated Learning Framework for 5G Networks](https://mlsanity.com/paper/2005.05752/) (2020)
- [When Federated Learning Meets Blockchain: A New Distributed Learning Paradigm](https://mlsanity.com/paper/2009.09338/) (2020)
- [Federated Learning for COVID-19 Detection with Generative Adversarial Networks in Edge Cloud Computing](https://mlsanity.com/paper/2110.07136/) (2021)
- [A Survey on the Security of Blockchain Systems](https://mlsanity.com/paper/1802.06993/) (2018)
- [A Flexible Machine Learning-Aware Architecture for Future WLANs](https://mlsanity.com/paper/1910.03510/) (2019)
- [Ownership preserving AI Market Places using Blockchain](https://mlsanity.com/paper/2001.09011/) (2020)
- [BEdgeHealth: A Decentralized Architecture for Edge-based IoMT Networks Using Blockchain](https://mlsanity.com/paper/2109.14295/) (2021)
- [Discrete-Time Analysis of Wireless Blockchain Networks](https://mlsanity.com/paper/2104.05586/) (2021)
- [On the Performance of Blockchain-enabled RAN-as-a-service in Beyond 5G Networks](https://mlsanity.com/paper/2105.14221/) (2021)

## Built on by (most cited first, 2 of 2)

- [A Fast Blockchain-based Federated Learning Framework with Compressed Communications](https://mlsanity.com/paper/2208.06095/) (2022)
- [The Implications of Decentralization in Blockchained Federated Learning: Evaluating the Impact of Model Staleness and Inconsistencies](https://mlsanity.com/paper/2310.07471/) (2023)

## Abstract (arXiv)

Motivated by the heterogeneous nature of devices participating in large-scale Federated Learning (FL) optimization, we focus on an asynchronous server-less FL solution empowered by blockchain technology. In contrast to mostly adopted FL approaches, which assume synchronous operation, we advocate an asynchronous method whereby model aggregation is done as clients submit their local updates. The asynchronous setting fits well with the federated optimization idea in practical large-scale settings with heterogeneous clients. Thus, it potentially leads to higher efficiency in terms of communication overhead and idle periods. To evaluate the learning completion delay of BC-enabled FL, we provide an analytical model based on batch service queue theory. Furthermore, we provide simulation results to assess the performance of both synchronous and asynchronous mechanisms. Important aspects involved in the BC-enabled FL optimization, such as the network size, link capacity, or user requirements, are put together and analyzed. As our results show, the synchronous setting leads to higher prediction accuracy than the asynchronous case. Nevertheless, asynchronous federated optimization provides much lower latency in many cases, thus becoming an appealing solution for FL when dealing with large datasets, tough timing constraints (e.g., near-real-time applications), or highly varying training data.