RECESS Vaccine for Federated Learning: Proactive Defense Against Model Poisoning Attacks
Authors: Haonan Yan, Wenjing Zhang, Qian Chen, Xiaoguang Li, Wenhai Sun, HUI LI, Xiaodong Lin
NeurIPS 2023 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Finally, we extensively evaluate RECESS on typical model architectures and four datasets under various settings... Experimental results show the superiority of RECESS in terms of reducing accuracy loss caused by the latest model poisoning attacks over five classic and two state-of-the-art defenses. |
| Researcher Affiliation | Academia | 1Xidian University, 2University of Guelph, 3Purdue University |
| Pseudocode | No | The paper includes a section titled 'Proactive Detection Algorithm' describing steps, but it is not formatted as a pseudocode block or a clearly labeled algorithm. |
| Open Source Code | No | The paper does not contain an explicit statement about releasing its source code, nor does it provide a direct link to a code repository for the methodology described. |
| Open Datasets | Yes | Table 1 shows four datasets and parameter settings used in the evaluation. ... MNIST, CIFAR-10, Purchase, FEMNIST. |
| Dataset Splits | No | The paper mentions using IID and Non-IID dataset divisions and references other works for dataset construction methods, but it does not explicitly provide specific percentages or counts for training, validation, or test dataset splits. |
| Hardware Specification | No | The paper does not provide any specific details about the hardware (e.g., GPU models, CPU types, memory) used to conduct the experiments. |
| Software Dependencies | No | The paper mentions optimizers (Adam, SGD) but does not provide specific software names with version numbers for libraries, frameworks, or environments used in the experiments. |
| Experiment Setup | Yes | Table 1: Experiment datasets and FL settings. ... Dataset... Model... Clients... Batch Size... Optimizer... Learning Rates... Epochs. For RECESS, we set A = 0.95, TS0 = 1, and baseline_decreased_score = 0.1 unless otherwise specified. |