FedCD: A Fairness-aware Federated Cognitive Diagnosis Framework

TL;DR

FedCD is a federated learning framework designed for educational data that enhances fairness and privacy in cognitive diagnosis by decoupling model parameters for personalized and shared components, improving diagnosis accuracy across diverse student groups.

Contribution

This paper introduces FedCD, a novel federated learning framework with a parameter decoupling strategy to improve fairness and privacy in cognitive diagnosis models across multiple educational institutions.

Findings

FedCD outperforms five FL approaches in experiments.

The personalization strategy improves diagnosis fairness.

Decoupling parameters enhances model accuracy and fairness.

Abstract

Online intelligent education platforms have generated a vast amount of distributed student learning data. This influx of data presents opportunities for cognitive diagnosis (CD) to assess students' mastery of knowledge concepts while also raising significant data privacy and security challenges. To cope with this issue, federated learning (FL) becomes a promising solution by jointly training models across multiple local clients without sharing their original data. However, the data quality problem, caused by the ability differences and educational context differences between different groups/schools of students, further poses a challenge to the fairness of models. To address this challenge, this paper proposes a fairness-aware federated cognitive diagnosis framework (FedCD) to jointly train CD models built upon a novel parameter decoupling-based personalization strategy, preserving privacy of data and achieving precise and fair diagnosis of students on each client. As an FL paradigm, FedCD trains a local CD model for the students in each client based on its local student learning data, and each client uploads its partial model parameters to the central server for parameter aggregation according to the devised innovative personalization strategy. The main idea of this strategy is to decouple model parameters into two parts: the first is used as locally personalized parameters, containing diagnostic function-related model parameters, to diagnose each client's students fairly; the second is the globally shared parameters across clients and the server, containing exercise embedding parameters, which are updated via fairness-aware aggregation, to alleviate inter-school unfairness. Experiments on three real-world datasets demonstrate the effectiveness of the proposed FedCD framework and the personalization strategy compared to five FL approaches under three CD models.