Federated learning enables collaborative training while preserving the privacy of all participants. However, the heterogeneity in data distribution across multiple training nodes poses significant challenges to the construction of federated models. Prior studies were dedicated to mitigating the effects of data heteroge...
Sheng Huang, Le-Le Fu, Fanghua Ye et al.· Neural Information Processin...· 4 citations
Multimodal federated learning (MFL) has emerged as a pivotal paradigm for leveraging distributed data to enhance model performance. However, existing methods predominantly rely on idealized assumptions of model homogeneity and balanced modality distributions, rendering them ill-suited for practical scenarios characteri...
One-shot federated graph learning (FGL) requires the server to estimate client contributions from highly compressed information, yet conventional volume-based weighting captures the amount of client data while overlooking how its connectivity is organized. In this paper, we propose SPIRE, a Structural Entropy-Driven Gr...
Shu-Tong Zheng, Le-Le Fu, Sheng Huang et al.· 0 citations
FedSPA, a Subspace Projection Aggregation personalized differential private Federated learning framework, is proposed, which not only effectively guides the aggregation of client personalized differential privacy but also reduces communication overhead.
Tianchi Liao, Xiaojun Deng, Le-Le Fu et al.· Proceedings of the 32nd ACM...· 0 citations
Differential privacy (DP) mechanisms have been widely adopted in federated learning (FL) to enhance model security. However, existing approaches predominantly employ uniform privacy budgets, neglecting personalized requirements arising from heterogeneous user privacy preferences. Such uniform privacy configurations typ...
Tianchi Liao, Xiaojun Deng, Lele Fu et al.· Proceedings of the 32nd ACM...· 0 citations
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