3 papers
cs.LG2026
TOFD: Target-Oriented Feature Decoupling against Poisoning Attacks in Split Federated Learning
Yuhan Xie, Jingrong Huang, Chen Lyu
Split Federated Learning (SFL) facilitates privacy-preserving collaborative training with reduced client-side overhead. However, its split architecture introduces unique attack sur…
cs.LG2026
BESplit: Bias-Compensated Split Federated Learning with Evidential Aggregation
Yuhan Xie, Chen Lyu, Jingrong Huang
Split Federated Learning (SFL) enables privacy-preserving collaborative training by partitioning models between clients and a server. However, under non-IID data distributions, SFL…
cs.LG2025
HealSplit: Towards Self-Healing through Adversarial Distillation in Split Federated Learning
Yuhan Xie, Chen Lyu
Split Federated Learning (SFL) is an emerging paradigm for privacy-preserving distributed learning. However, it remains vulnerable to sophisticated data poisoning attacks targeting…