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cs.LG2024
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…
cs.LG2024
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…
cs.LG2024
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…