# BAFFLE : Blockchain Based Aggregator Free Federated Learning

arXiv: https://arxiv.org/abs/1909.07452 · HTML: https://mlsanity.com/paper/1909.07452/

## Key facts

- Cited by 38 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 16 Sep 2019.

## Built on by (most cited first, 30 of 38)

- [Federated Learning: Opportunities and Challenges](https://mlsanity.com/paper/2101.05428/) (2021)
- [Federated Quantum Machine Learning](https://mlsanity.com/paper/2103.12010/) (2021)
- [Federated Learning for Medical Applications: A Taxonomy, Current Trends, Challenges, and Future Research Directions](https://mlsanity.com/paper/2208.03392/) (2022)
- [IPLS : A Framework for Decentralized Federated Learning](https://mlsanity.com/paper/2101.01901/) (2021)
- [Decentralized Federated Learning: A Survey on Security and Privacy](https://mlsanity.com/paper/2401.17319/) (2024)
- [Fairness, Integrity, and Privacy in a Scalable Blockchain-based Federated Learning System](https://mlsanity.com/paper/2111.06290/) (2021)
- [A Systematic Survey of Blockchained Federated Learning](https://mlsanity.com/paper/2110.02182/) (2021)
- [Threats and Defenses in Federated Learning Life Cycle: A Comprehensive Survey and Challenges](https://mlsanity.com/paper/2407.06754/) (2024)
- [Robust Blockchained Federated Learning with Model Validation and Proof-of-Stake Inspired Consensus](https://mlsanity.com/paper/2101.03300/) (2021)
- [Incentive Mechanism Design for Joint Resource Allocation in Blockchain-based Federated Learning](https://mlsanity.com/paper/2202.10938/) (2022)
- [Enhancing Trust and Privacy in Distributed Networks: A Comprehensive Survey on Blockchain-based Federated Learning](https://mlsanity.com/paper/2403.19178/) (2024)
- [Demystifying Swarm Learning: A New Paradigm of Blockchain-based Decentralized Federated Learning](https://mlsanity.com/paper/2201.05286/) (2022)
- [BEAS: Blockchain Enabled Asynchronous & Secure Federated Machine Learning](https://mlsanity.com/paper/2202.02817/) (2022)
- [Blockchained Federated Learning for Internet of Things: A Comprehensive Survey](https://mlsanity.com/paper/2305.04513/) (2023)
- [Blockchain-empowered Federated Learning: Benefits, Challenges, and Solutions](https://mlsanity.com/paper/2403.00873/) (2024)
- [PQS-BFL: A Post-Quantum Secure Blockchain-based Federated Learning Framework](https://mlsanity.com/paper/2505.01866/) (2025)
- [DeFL: Decentralized Weight Aggregation for Cross-silo Federated Learning](https://mlsanity.com/paper/2208.00848/) (2022)
- [Use of Federated Learning and Blockchain towards Securing Financial Services](https://mlsanity.com/paper/2303.12944/) (2023)
- [Communication-Efficient Training Workload Balancing for Decentralized Multi-Agent Learning](https://mlsanity.com/paper/2405.00839/) (2024)
- [Decentralized Intelligence Network (DIN)](https://mlsanity.com/paper/2407.02461/) (2024)
- [Blockchain Meets LLMs: A Living Survey on Bidirectional Integration](https://mlsanity.com/paper/2411.16809/) (2024)
- [SCEI: A Smart-Contract Driven Edge Intelligence Framework for IoT Systems](https://mlsanity.com/paper/2103.07050/) (2021)
- [A Survey on Blockchain-Based Federated Learning and Data Privacy](https://mlsanity.com/paper/2306.17338/) (2023)
- [zkDFL: An efficient and privacy-preserving decentralized federated learning with zero-knowledge proof](https://mlsanity.com/paper/2312.04579/) (2023)
- [PDFed: Privacy-Preserving and Decentralized Asynchronous Federated Learning for Diffusion Models](https://mlsanity.com/paper/2409.18245/) (2024)
- [TrustChain: A Blockchain Framework for Auditing and Verifying Aggregators in Decentralized Federated Learning](https://mlsanity.com/paper/2502.16406/) (2025)
- [Decentralized Trust for Space AI: Blockchain-Based Federated Learning Across Multi-Vendor LEO Satellite Networks](https://mlsanity.com/paper/2512.08882/) (2025)
- [Leveraging Centric Data Federated Learning Using Blockchain For Integrity Assurance](https://mlsanity.com/paper/2206.04731/) (2022)
- [BPFISH: Blockchain and Privacy-preserving FL Inspired Smart Healthcare](https://mlsanity.com/paper/2207.11654/) (2022)
- [A Blockchain-based Platform for Reliable Inference and Training of Large-Scale Models](https://mlsanity.com/paper/2305.04062/) (2023)

## Abstract (arXiv)

A key aspect of Federated Learning (FL) is the requirement of a centralized aggregator to maintain and update the global model. However, in many cases orchestrating a centralized aggregator might be infeasible due to numerous operational constraints. In this paper, we introduce BAFFLE, an aggregator free, blockchain driven, FL environment that is inherently decentralized. BAFFLE leverages Smart Contracts (SC) to coordinate the round delineation, model aggregation and update tasks in FL. BAFFLE boosts computational performance by decomposing the global parameter space into distinct chunks followed by a score and bid strategy. In order to characterize the performance of BAFFLE, we conduct experiments on a private Ethereum network and use the centralized and aggregator driven methods as our benchmark. We show that BAFFLE significantly reduces the gas costs for FL on the blockchain as compared to a direct adaptation of the aggregator based method. Our results also show that BAFFLE achieves high scalability and computational efficiency while delivering similar accuracy as the benchmark methods.