# FLoBC: A Decentralized Blockchain-Based Federated Learning Framework

arXiv: https://arxiv.org/abs/2112.11873 · HTML: https://mlsanity.com/paper/2112.11873/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- Builds on 10 of its 22 references that are in the map.
- First posted on arXiv 22 Dec 2021.

## Builds on (most cited first, 10 of 10)

- [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)
- [Learning to learn by gradient descent by gradient descent](https://mlsanity.com/paper/1606.04474/) (2016)
- [SplitFed: When Federated Learning Meets Split Learning](https://mlsanity.com/paper/2004.12088/) (2020)
- [Distributed learning of deep neural network over multiple agents](https://mlsanity.com/paper/1810.06060/) (2018)
- [A Survey on Distributed Machine Learning](https://mlsanity.com/paper/1912.09789/) (2019)
- [Blockchained On-Device Federated Learning](https://mlsanity.com/paper/1808.03949/) (2018)
- [Peer-to-peer Federated Learning on Graphs](https://mlsanity.com/paper/1901.11173/) (2019)
- [A Blockchain-based Decentralized Federated Learning Framework with Committee Consensus](https://mlsanity.com/paper/2004.00773/) (2020)
- [Decentralized Federated Learning: A Segmented Gossip Approach](https://mlsanity.com/paper/1908.07782/) (2019)

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

- [Decentralized Federated Learning: Fundamentals, State of the Art, Frameworks, Trends, and Challenges](https://mlsanity.com/paper/2211.08413/) (2022)
- [Swarm Learning: A Survey of Concepts, Applications, and Trends](https://mlsanity.com/paper/2405.00556/) (2024)
- [A Robust Cross-Domain IDS using BiGRU-LSTM-Attention for Medical and Industrial IoT Security](https://mlsanity.com/paper/2508.12470/) (2025)
- [VeryFL: A Verify Federated Learning Framework Embedded with Blockchain](https://mlsanity.com/paper/2311.15617/) (2023)
- [A Lifecycle Cost Analysis of Smart-Contract-Coordinated Federated Learning Marketplaces](https://mlsanity.com/paper/2609.13170/) (2026)

## Abstract (arXiv)

The rapid expansion of data worldwide invites the need for more distributed solutions in order to apply machine learning on a much wider scale. The resultant distributed learning systems can have various degrees of centralization. In this work, we demonstrate our solution FLoBC for building a generic decentralized federated learning system using blockchain technology, accommodating any machine learning model that is compatible with gradient descent optimization. We present our system design comprising the two decentralized actors: trainer and validator, alongside our methodology for ensuring reliable and efficient operation of said system. Finally, we utilize FLoBC as an experimental sandbox to compare and contrast the effects of trainer-to-validator ratio, reward-penalty policy, and model synchronization schemes on the overall system performance, ultimately showing by example that a decentralized federated learning system is indeed a feasible alternative to more centralized architectures.