DPVS-Shapley:Faster and Universal Contribution Evaluation Component in Federated Learning

TL;DR

This paper introduces DPVS-Shapley, a faster and more adaptable method for evaluating participant contributions in federated learning, improving fairness and efficiency in contribution assessment.

Contribution

The paper presents DPVS-Shapley, a novel contribution evaluation component that accelerates Shapley value calculations through dynamic pruning and sample weighting.

Findings

Significantly reduces computation time for contribution evaluation.

Maintains high accuracy despite dataset pruning.

Allows weighting of samples based on difficulty.

Abstract

In the current era of artificial intelligence, federated learning has emerged as a novel approach to addressing data privacy concerns inherent in centralized learning paradigms. This decentralized learning model not only mitigates the risk of data breaches but also enhances the system's scalability and robustness. However, this approach introduces a new challenge: how to fairly and accurately assess the contribution of each participant. Developing an effective contribution evaluation mechanism is crucial for federated learning. Such a mechanism incentivizes participants to actively contribute their data and computational resources, thereby improving the overall performance of the federated learning system. By allocating resources and rewards based on the size of the contributions, it ensures that each participant receives fair treatment, fostering sustained engagement.Currently, Shapley value-based methods are widely used to evaluate participants' contributions, with many researchers proposing modifications to adapt these methods to real-world scenarios. In this paper, we introduce a component called Dynamic Pruning Validation Set Shapley (DPVS-Shapley). This method accelerates the contribution assessment process by dynamically pruning the original dataset without compromising the evaluation's accuracy. Furthermore, this component can assign different weights to various samples, thereby allowing clients capable of distinguishing difficult examples to receive higher contribution scores.