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Xin Chang

2 accepted papers

2026

AEFGL: Reverse Auction and Value Evaluation-Based Federated Graph Learning Incentive Mechanism (Student Abstract)

AAAI 2026technical

Federated Graph Learning enables multiple clients to collaboratively train graph models while protecting local private data. However, most studies have assumed that all clients contribute data voluntarily and actively. Without reasonable incentives, clients are often reluctant to contribute personal

Cited by 0SourcePDFScholar
2025

Martin: Mobility-Aware Reputation Mechanism for Federated Learning

ICASSP 2025accepted

The rapid development of the Internet of Things (IoT) has resulted in an increasing volume of data, much of which contains sensitive private information. Federated Learning (FL) allows clients to train models without sharing raw data, showing significant potential for privacy protection. However, de…

Cited by 0SourceScholar