דפסת חופשיות דינמית בלמידה רב-סילית דרך תבניות התקפה מומחזות

תקציר מקורי באנגליתarXiv:2604.04611v3 Announce Type: replace Abstract: Federated learning (FL) enables multiple clients to collaboratively train a global model by aggregating local updates without sharing private data. In this work, we focus on cross-silo FL, where each client typically represents an independent organization. However, cross-silo FL can face the challenge of free-riders, clients who submit fake model parameters without performing actual training to obtain the global model without contributing. Chen et al. proposed a free-rider detection method based on the weight evolving frequency (WEF) of model parameters. This detection approach is practical because it requires neither a proxy dataset nor pre-training. Nevertheless, it struggles to detect ``dynamic'' free-riders who behave honestly in earl