# Trust and Resilience in Federated Learning Through Smart Contracts Enabled Decentralized Systems

arXiv: https://arxiv.org/abs/2407.06862 · HTML: https://mlsanity.com/paper/2407.06862/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 4 of its 22 references that are in the map.
- First posted on arXiv 9 Jul 2024.

## Builds on (most cited first, 4 of 4)

- [Federated Optimization in Heterogeneous Networks](https://mlsanity.com/paper/1812.06127/) (2018)
- [On the Efficiency of Decentralized File Storage for Personal Information Management Systems](https://mlsanity.com/paper/2007.03505/) (2020)
- [Federated Learning using Smart Contracts on Blockchains, based on Reward Driven Approach](https://mlsanity.com/paper/2107.10243/) (2021)
- [Security Analysis of Distributed Ledgers and Blockchains through Agent-based Simulation](https://mlsanity.com/paper/2109.08358/) (2021)

## Built on by (most cited first, 1 of 1)

- [Trust-Based Incentive Mechanisms in Semi-Decentralized Federated Learning Systems](https://mlsanity.com/paper/2602.08290/) (2026)

## Abstract (arXiv)

In this paper, we present a study of a Federated Learning (FL) system, based on the use of decentralized architectures to ensure trust and increase reliability. The system is based on the idea that the FL collaborators upload the (ciphered) model parameters on the Inter-Planetary File System (IPFS) and interact with a dedicated smart contract to track their behavior. Thank to this smart contract, the phases of parameter updates are managed efficiently, thereby strengthening data security. We have carried out an experimental study that exploits two different methods of weight aggregation, i.e., a classic averaging scheme and a federated proximal aggregation. The results confirm the feasibility of the proposal.