Efficient Client Contribution Evaluation for Horizontal Federated Learning

TL;DR

This paper introduces an efficient method for evaluating individual client contributions in horizontal federated learning, leveraging reinforcement learning to improve accuracy and reduce computational costs compared to traditional methods.

Contribution

The paper presents a novel, computationally efficient approach for contribution evaluation in horizontal FL using reinforcement learning-based gradient estimation.

Findings

Outperforms leave-one-out method in valuation accuracy

Reduces computational complexity significantly

Demonstrates effectiveness through experimental results

Abstract

In federated learning (FL), fair and accurate measurement of the contribution of each federated participant is of great significance. The level of contribution not only provides a rational metric for distributing financial benefits among federated participants, but also helps to discover malicious participants that try to poison the FL framework. Previous methods for contribution measurement were based on enumeration over possible combination of federated participants. Their computation costs increase drastically with the number of participants or feature dimensions, making them inapplicable in practical situations. In this paper an efficient method is proposed to evaluate the contributions of federated participants. This paper focuses on the horizontal FL framework, where client servers calculate parameter gradients over their local data, and upload the gradients to the central server. Before aggregating the client gradients, the central server train a data value estimator of the gradients using reinforcement learning techniques. As shown by experimental results, the proposed method consistently outperforms the conventional leave-one-out method in terms of valuation authenticity as well as time complexity.