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cs.LG2026
FedCARE: Federated Unlearning with Conflict-Aware Projection and Relearning-Resistant Recovery
Yue Li, Mingmin Chu, Xilei Yang +5
Federated learning (FL) enables collaborative model training without centralizing raw data, but privacy regulations such as the right to be forgotten require FL systems to remove t…
cs.LG2025
MUDDFormer: Breaking Residual Bottlenecks in Transformers via Multiway Dynamic Dense Connections
Da Xiao, Qingye Meng, Shengping Li +1
We propose MUltiway Dynamic Dense (MUDD) connections, a simple yet effective method to address the limitations of residual connections and enhance cross-layer information flow in T…
cs.LG2024★ 1 cited
Improving Transformers with Dynamically Composable Multi-Head Attention
Da Xiao, Qingye Meng, Shengping Li +1
Multi-Head Attention (MHA) is a key component of Transformer. In MHA, attention heads work independently, causing problems such as low-rank bottleneck of attention score matrices a…