# A Lifecycle Cost Analysis of Smart-Contract-Coordinated Federated Learning Marketplaces

arXiv: https://arxiv.org/abs/2609.13170 · HTML: https://mlsanity.com/paper/2609.13170/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 14 of its 24 references that are in the map.
- First posted on arXiv 23 Jul 2026.

## Builds on (most cited first, 14 of 14)

- [Communication-Efficient Learning of Deep Networks from Decentralized Data](https://mlsanity.com/paper/1602.05629/) (2016)
- [Flower: A Friendly Federated Learning Research Framework](https://mlsanity.com/paper/2007.14390/) (2020)
- [Blockchained On-Device Federated Learning](https://mlsanity.com/paper/1808.03949/) (2018)
- [IPFS - Content Addressed, Versioned, P2P File System](https://mlsanity.com/paper/1407.3561/) (2014)
- [FedCoin: A Peer-to-Peer Payment System for Federated Learning](https://mlsanity.com/paper/2002.11711/) (2020)
- [When Federated Learning Meets Blockchain: A New Distributed Learning Paradigm](https://mlsanity.com/paper/2009.09338/) (2020)
- [Smart Policy Control for Securing Federated Learning Management System](https://mlsanity.com/paper/2305.09134/) (2023)
- [FLoBC: A Decentralized Blockchain-Based Federated Learning Framework](https://mlsanity.com/paper/2112.11873/) (2021)
- [Blockchain-Based Federated Learning: Incentivizing Data Sharing and Penalizing Dishonest Behavior](https://mlsanity.com/paper/2307.10492/) (2023)
- [Blockchain-Enabled Federated Learning: A Reference Architecture Design, Implementation, and Verification](https://mlsanity.com/paper/2306.10841/) (2023)
- [PQS-BFL: A Post-Quantum Secure Blockchain-based Federated Learning Framework](https://mlsanity.com/paper/2505.01866/) (2025)
- [Blockchain-based Framework for Scalable and Incentivized Federated Learning](https://mlsanity.com/paper/2502.14170/) (2025)
- [FEDSTR: Money-In AI-Out | A Decentralized Marketplace for Federated Learning and LLM Training on the NOSTR Protocol](https://mlsanity.com/paper/2404.15834/) (2024)
- [Incentive-Based Federated Learning: Architectural Elements and Future Directions](https://mlsanity.com/paper/2510.14208/) (2025)

## Abstract (arXiv)

Blockchain-enabled Federated Learning (FL) marketplaces enable collaborative model training among mutually distrustful participants through smart contracts. Although numerous architectures exist, their economic evaluation is typically limited to isolated blockchain operations rather than the complete marketplace lifecycle. Consequently, it remains unclear whether operational costs depend on operating at scale. This paper presents an experimental study of the operational cost of a DAO-governed marketplace. Our evaluation decomposes the gas consumption of every blockchain operation throughout the contract lifecycle, performs ablation experiments to isolate the impact of on-chain coordination and IPFS storage on federated training, and derives an analytical model describing the amortization of deployment costs. Results show the lifecycle of a single training task consumes approximately 3.8 million gas units per hired trainer. The average cost per training round reaches its amortization knee - defined as twice the asymptotic recurring cost - after approximately 20 communication rounds. Moreover, integrating smart contracts and IPFS preserves model performance, achieving accuracy comparable to conventional FL deployments. These findings demonstrate that smart-contract-coordinated FL marketplaces exhibit an amortizing cost structure not because on-chain operations are inexpensive, but because recurring costs are one to two orders of magnitude smaller than fixed deployment costs, thereby diluting over the federation's lifetime.