FedCoin: A Peer-to-Peer Payment System for Federated Learning

By Y. Liu, Shuai Sun, Zhengpeng Ai, Shuangfeng Zhang, Zelei Liu, Han Yu. 2020

133 citations recorded by the source corpus.

Federated learning (FL) is an emerging collaborative machine learning method to train models on distributed datasets with privacy concerns. To properly incentivize data owners to contribute their efforts, Shapley Value (SV) is often adopted to fairly assess their contribution. However, the calculation of SV is time-consuming and computationally costly. In this paper, we propose FedCoin, a blockchain-based peer-to-peer payment system for FL to enable a feasible SV based profit distribution. In FedCoin, blockchain consensus entities calculate SVs and a new block is created based on the proof of Shapley (PoSap) protocol. It is in contrast to the popular BitCoin network where consensus entities "mine" new blocks by solving meaningless puzzles. Based on the computed SVs, a scheme for dividing the incentive payoffs among FL clients with nonrepudiation and tamper-resistance properties is proposed. Experimental results based on real-world data show that FedCoin can promote high-quality data from FL clients through accurately computing SVs with an upper bound on the computational resources required for reaching consensus. It opens opportunities for non-data owners to play a role in FL.

Subfields: Artificial Intelligence, Computer Science Applications, Information Systems

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Artificial Intelligence

1. Murali Annavaram
2. Qiang Yang
3. Chaoyang He
4. Yang Liu
5. S. Avestimehr
6. Praneeth Vepakomma
7. Abhishek Singh
8. Ramesh Raskar

In Computer Science Applications

1. Chris J. M. Yoon
2. Peilin Zhao
3. Murali Annavaram
4. Ramesh Raskar
5. Qiang Yang
6. Clement Fung
7. Ivan Beschastnikh
8. Praneeth Vepakomma

In Information Systems

1. Chris J. M. Yoon
2. Peilin Zhao
3. Jinhyun So
4. Ramesh Raskar
5. Yan Kang
6. Qiang Yang
7. Clement Fung
8. Songze Li

Citations in the corpus

• FedML: A Research Library and Benchmark for Federated Machine Learning (2020, background): “…application domains include, but are not limited to, computer vision [77, 78], natural language processing [79, 80, 81, 82, 83], finance [74, 35, 84], transportation [85, 86, 87, 88, 89, 90, 91, 92, 93, 94], digital health [95, 96, 97, 98, 99, 100, 101], recommendation [102, 103, 104, 105, 106,…”
• Biscotti: A Blockchain System for Private and Secure Federated Learning (2021, background): “Although a variety of recent work has proposed the use of blockchain systems for distributed learning [38], [39], [40], [83], [84], [85], [86], Biscotti addresses a unique point in the space of solutions.”