# Transparent Contribution Evaluation for Secure Federated Learning on Blockchain

arXiv: https://arxiv.org/abs/2101.10572 · HTML: https://mlsanity.com/paper/2101.10572/

## Key facts

- Cited by 16 papers in the map (data through 14 Oct 2026).
- Builds on 11 of its 23 references that are in the map.
- First posted on arXiv 26 Jan 2021.

## Builds on (most cited first, 11 of 11)

- [Communication-Efficient Learning of Deep Networks from Decentralized Data](https://mlsanity.com/paper/1602.05629/) (2016)
- [Deep Leakage from Gradients](https://mlsanity.com/paper/1906.08935/) (2019)
- [Data Shapley: Equitable Valuation of Data for Machine Learning](https://mlsanity.com/paper/1904.02868/) (2019)
- [Federated Machine Learning: Concept and Applications](https://mlsanity.com/paper/1902.04885/) (2019)
- [Towards Efficient Data Valuation Based on the Shapley Value](https://mlsanity.com/paper/1902.10275/) (2019)
- [A Principled Approach to Data Valuation for Federated Learning](https://mlsanity.com/paper/2009.06192/) (2020)
- [Wireless Federated Learning with Local Differential Privacy](https://mlsanity.com/paper/2002.05151/) (2020)
- [Collecting and Analyzing Multidimensional Data with Local Differential Privacy](https://mlsanity.com/paper/1907.00782/) (2019)
- [FedSel: Federated SGD under Local Differential Privacy with Top-k Dimension Selection](https://mlsanity.com/paper/2003.10637/) (2020)
- [FLAME: Differentially Private Federated Learning in the Shuffle Model](https://mlsanity.com/paper/2009.08063/) (2020)
- [2CP: Decentralized Protocols to Transparently Evaluate Contributivity in Blockchain Federated Learning Environments](https://mlsanity.com/paper/2011.07516/) (2020)

## Built on by (most cited first, 16 of 16)

- [Incentive Mechanisms for Federated Learning: From Economic and Game Theoretic Perspective](https://mlsanity.com/paper/2111.11850/) (2021)
- [Advances and Open Challenges in Federated Foundation Models](https://mlsanity.com/paper/2404.15381/) (2024)
- [Decentral and Incentivized Federated Learning Frameworks: A Systematic Literature Review](https://mlsanity.com/paper/2205.07855/) (2022)
- [Towards Interpretable Federated Learning](https://mlsanity.com/paper/2302.13473/) (2023)
- [Enhancing Trust and Privacy in Distributed Networks: A Comprehensive Survey on Blockchain-based Federated Learning](https://mlsanity.com/paper/2403.19178/) (2024)
- [A Survey of Federated Evaluation in Federated Learning](https://mlsanity.com/paper/2305.08070/) (2023)
- [Proof-of-Contribution-Based Design for Collaborative Machine Learning on Blockchain](https://mlsanity.com/paper/2302.14031/) (2023)
- [Blockchained Federated Learning for Internet of Things: A Comprehensive Survey](https://mlsanity.com/paper/2305.04513/) (2023)
- [Exploring Data Redundancy in Real-world Image Classification through Data Selection](https://mlsanity.com/paper/2306.14113/) (2023)
- [Contribution Evaluation in Federated Learning: Examining Current Approaches](https://mlsanity.com/paper/2311.09856/) (2023)
- [A Comprehensive Study of Shapley Value in Data Analytics](https://mlsanity.com/paper/2412.01460/) (2024)
- [Detect & Score: Privacy-Preserving Misbehaviour Detection and Contribution Evaluation in Federated Learning](https://mlsanity.com/paper/2506.23583/) (2025)
- [Private and Robust Contribution Evaluation in Federated Learning](https://mlsanity.com/paper/2602.21721/) (2026)
- [DPVS-Shapley:Faster and Universal Contribution Evaluation Component in Federated Learning](https://mlsanity.com/paper/2410.15093/) (2024)
- [Crowd-SFT: Crowdsourcing for LLM Alignment](https://mlsanity.com/paper/2506.04063/) (2025)
- [Knowledge-Free Correlated Agreement for Incentivizing Federated Learning](https://mlsanity.com/paper/2605.04747/) (2026)

## Abstract (arXiv)

Federated Learning is a promising machine learning paradigm when multiple parties collaborate to build a high-quality machine learning model. Nonetheless, these parties are only willing to participate when given enough incentives, such as a fair reward based on their contributions. Many studies explored Shapley value based methods to evaluate each party's contribution to the learned model. However, they commonly assume a semi-trusted server to train the model and evaluate the data owners' model contributions, which lacks transparency and may hinder the success of federated learning in practice. In this work, we propose a blockchain-based federated learning framework and a protocol to transparently evaluate each participant's contribution. Our framework protects all parties' privacy in the model building phase and transparently evaluates contributions based on the model updates. The experiment with the handwritten digits dataset demonstrates that the proposed method can effectively evaluate the contributions.