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

Lorenzo Cassano, Jacopo D'Abramo, Siraj Munir, Stefano Ferretti
7/9/2024
cs.AIcs.LG

No confident code implementation found for this paper.

Abstract

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.

View on arXivView PDF

Code Implementations

No confident code match yet

We couldn't find an author-owned or strongly-evidenced community implementation for this paper. Any repos shown below are weak matches — verify before relying on them.

No code implementations found yet.

Know of an implementation? Let us know in the comments below!

Similar Papers

Resilient Federated Chain: Transforming Blockchain Consensus into an Active Defense Layer for Federated Learning

85% match2026

Defending Against Poisoning Attacks in Federated Learning with Blockchain

85% match2023

Don't Reach for the Stars: Rethinking Topology for Resilient Federated Learning

84% match2025

Secure Decentralized Learning with Blockchain

84% match2023

FedStrategist: A Meta-Learning Framework for Adaptive and Robust Aggregation in Federated Learning

84% match2025

Smart Policy Control for Securing Federated Learning Management System

84% match2023

Cite this paper

@article{cassano2024trust,
  title  = {Trust and Resilience in Federated Learning Through Smart Contracts Enabled Decentralized Systems},
  author = {Lorenzo Cassano and Jacopo D'Abramo and Siraj Munir and Stefano Ferretti},
  year   = {2024},
  eprint = {2407.06862},
  archivePrefix = {arXiv},
  url    = {http://arxiv.org/abs/2407.06862v1}
}

Add a badge to your README

Shows whether official code is linked for this paper.

Code of Paper badge
[![Code of Paper](https://codeofpaper.com/badge/2407.06862.svg)](https://codeofpaper.com/paper/2407.06862)

Discussion