AI RESEARCH
FEDBUD: Joint Incentive and Privacy Optimization for Resource-Constrained Federated Learning
arXiv CS.LG
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ArXi:2604.10499v1 Announce Type: cross Federated learning has become a popular paradigm for privacy protection and edge-based machine learning. However, defending against differential attacks and devising incentive strategies remain significant bottlenecks in this field. Despite recent works on privacy-aware incentive mechanism design for federated learning, few of them consider both data volume and noise level.