5 citations · 9 across the 14 of their papers we have counts for
7 papers · 1 filter
On the Out-of-Distribution Backdoor Attack for Federated Learning
Jiahao Xu, Zikai Zhang, Rui Hu
Traditional backdoor attacks in federated learning (FL) operate within constrained attack scenarios, as they depend on visible triggers and require physical modifications to the ta…
Fed-HeLLo: Efficient Federated Foundation Model Fine-Tuning with Heterogeneous LoRA Allocation
Zikai Zhang, Ping Liu, Jiahao Xu +1
Federated Learning has recently been utilized to collaboratively fine-tune foundation models across multiple clients. Notably, federated low-rank adaptation LoRA-based fine-tuning…
Detecting Backdoor Attacks in Federated Learning via Direction Alignment Inspection
Jiahao Xu, Zikai Zhang, Rui Hu
The distributed nature of training makes Federated Learning (FL) vulnerable to backdoor attacks, where malicious model updates aim to compromise the global model's performance on s…
Identify Backdoored Model in Federated Learning via Individual Unlearning
Jiahao Xu, Zikai Zhang, Rui Hu
Backdoor attacks present a significant threat to the robustness of Federated Learning (FL) due to their stealth and effectiveness. They maintain both the main task of the FL system…
Fed-pilot: Optimizing LoRA Allocation for Efficient Federated Fine-Tuning with Heterogeneous Clients
Zikai Zhang, Rui Hu, Ping Liu +1
Federated Learning enables the fine-tuning of foundation models (FMs) across distributed clients for specific tasks; however, its scalability is limited by the heterogeneity of cli…
Achieving Byzantine-Resilient Federated Learning via Layer-Adaptive Sparsified Model Aggregation
Jiahao Xu, Zikai Zhang, Rui Hu
Federated Learning (FL) enables multiple clients to collaboratively train a model without sharing their local data. Yet the FL system is vulnerable to well-designed Byzantine attac…