A REAL-TIME CONTRIBUTION MEASUREMENT METHOD FOR PARTICIPANTS IN FEDERATED LEARN-ING

Abstract

Federated learning is a framework for protecting distributed data privacy and has participated in commercial activities. However, there is a lack of a sufficiently reasonable contribution measurement mechanism to distribute the reward for each agent. In the commercial union, if there is no mechanism like this, every agent will get the same reward. This is unfair to agents that provide better data, so such a mechanism is needed. To address this issue, this work proposes a real-time contribution measurement method. Firstly, the method defines the impact of each agent. Furthermore, we comprehensively consider the current round and the previous round to obtain the contribution rate of each agent. To verify effectiveness of the proposed method, the work conducts pseudo-distributed training and an experiment on the Penn Treebank dataset. Comparing the Shapley Value in game theory, the comparative experiment result shows that the proposed method is more sensitive to both data quantity and data quality under the premise of maintaining real-time.

1. INTRODUCTION

There are lots of data generate, collect, and access every day by smart terminals. But cause of the privacy of these data, it is usually difficult to use them. Such as the language model to predict the next word or even entire reply (Ion et al., 2016) . 2020) proposed an incentive mechanism to make each agent willing to contribute better data. But most of them need to consume large computing resources and many methods are calculated offline. In order to address this problem, this paper proposes a method for obtaining the contribution of each agent in real time with a small amount of calculation in horizontal federated learning. Our contributions in this paper are as follows: • We propose a method to measure agents' contributions and compare this method with Shapley Value. • The method we propose is sensitive to data volume and data quality, and can be used for mutual comparison between agents. • In the training process, the contribution to each agent can be obtained in real time, with low computational complexity.



The emergence of federated learning breaks this data barrier. It can use agent computing power to conduct model training while maintaining data localization and privacy protection, and obtain an excellent global model. But in a commercial federation, each agent should get corresponding rewards based on its contribution to the model, not the same rewards. There are many methods for contribution measurement. Such Wang et al. (2019) measured the contribution of each group features in vertical federated learning, and Zhan et al. (

