Jiahui Lin

Sports images sit with schools, clubs and other organizations, and federated learning avoids sending raw pictures but still transmits model updates that can leak information, while the clipping and noise of differential privacy damage useful knowledge. FedSS scores each network stage for how shareable it is before clipping, uploads only the perturbed cross-client sport semantics, and keeps client-specific appearance and background context local. On sport22 and sport100 it beats the strongest baseline DPSIR-Fed by 5.78 and 5.71 points under visual-context heterogeneity, and raises sport22 accuracy from 42.67% to 46.81% at the strong-privacy setting ε=4.

Sports images sit with schools, clubs and other organizations, and federated learning avoids sending raw pictures but still transmits model updates that can leak information, while the clipping and noise of differential privacy damage useful knowledge. FedSS scores each network stage for how shareable it is before clipping, uploads only the perturbed cross-client sport semantics, and keeps client-specific appearance and background context local. On sport22 and sport100 it beats the strongest baseline DPSIR-Fed by 5.78 and 5.71 points under visual-context heterogeneity, and raises sport22 accuracy from 42.67% to 46.81% at the strong-privacy setting ε=4.