# Enhancing Scalability and Reliability in Semi-Decentralized Federated Learning With Blockchain: Trust Penalization and Asynchronous Functionality

arXiv: https://arxiv.org/abs/2310.19287 · HTML: https://mlsanity.com/paper/2310.19287/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 10 of its 18 references that are in the map.
- First posted on arXiv 30 Oct 2023.

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

- [Towards Federated Learning at Scale: System Design](https://mlsanity.com/paper/1902.01046/) (2019)
- [Decentralized Federated Learning: Fundamentals, State of the Art, Frameworks, Trends, and Challenges](https://mlsanity.com/paper/2211.08413/) (2022)
- [Semi-Decentralized Federated Learning with Cooperative D2D Local Model Aggregations](https://mlsanity.com/paper/2103.10481/) (2021)
- [Opportunities of Federated Learning in Connected, Cooperative and Automated Industrial Systems](https://mlsanity.com/paper/2101.03367/) (2021)
- [COLA: Decentralized Linear Learning](https://mlsanity.com/paper/1808.04883/) (2018)
- [Semi-Decentralized Federated Learning with Collaborative Relaying](https://mlsanity.com/paper/2205.10998/) (2022)
- [D-Cliques: Compensating for Data Heterogeneity with Topology in Decentralized Federated Learning](https://mlsanity.com/paper/2104.07365/) (2021)
- [Jointly Learning from Decentralized (Federated) and Centralized Data to Mitigate Distribution Shift](https://mlsanity.com/paper/2111.12150/) (2021)
- [Blockchain-Based Federated Learning: Incentivizing Data Sharing and Penalizing Dishonest Behavior](https://mlsanity.com/paper/2307.10492/) (2023)
- [User Data Sharing Frameworks: A Blockchain-Based Incentive Solution](https://mlsanity.com/paper/1910.11927/) (2019)

## Built on by (most cited first, 3 of 3)

- [Navigating AI to Unpack Youth Privacy Concerns: An In-Depth Exploration and Systematic Review](https://mlsanity.com/paper/2412.16369/) (2024)
- [Toward Ethical AI: A Qualitative Analysis of Stakeholder Perspectives](https://mlsanity.com/paper/2501.13320/) (2025)
- [Trust-Based Incentive Mechanisms in Semi-Decentralized Federated Learning Systems](https://mlsanity.com/paper/2602.08290/) (2026)

## Abstract (arXiv)

The paper presents an innovative approach to address the challenges of scalability and reliability in Distributed Federated Learning by leveraging the integration of blockchain technology. The paper focuses on enhancing the trustworthiness of participating nodes through a trust penalization mechanism while also enabling asynchronous functionality for efficient and robust model updates. By combining Semi-Decentralized Federated Learning with Blockchain (SDFL-B), the proposed system aims to create a fair, secure and transparent environment for collaborative machine learning without compromising data privacy. The research presents a comprehensive system architecture, methodologies, experimental results, and discussions that demonstrate the advantages of this novel approach in fostering scalable and reliable SDFL-B systems.