Robust Federated Learning: The Case of Affine Distribution Shifts
Authors: Amirhossein Reisizadeh, Farzan Farnia, Ramtin Pedarsani, Ali Jadbabaie
NeurIPS 2020 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We perform several numerical experiments to empirically support FLRA. We show that an affine distribution shift indeed suffices to significantly decrease the performance of the learnt classifier in a new test user, and our proposed algorithm achieves a significant gain in comparison to standard federated learning and adversarial training methods. |
| Researcher Affiliation | Academia | Amirhossein Reisizadeh ECE Department UC Santa Barbara reisizadeh@ucsb.edu Farzan Farnia MIT farnia@mit.edu Ramtin Pedarsani ECE Department UC Santa Barbara ramtin@ece.ucsb.edu Ali Jadbabaie MIT jadbabai@mit.edu |
| Pseudocode | Yes | Algorithm 1 Fed Robust |
| Open Source Code | No | The paper does not provide an unambiguous statement about releasing source code for their methodology or a link to a repository. |
| Open Datasets | Yes | We considered the standard MNIST [38] and CIFAR-10 [39] datasets and used three standard neural network architectures in the literature: Alex Net [40], Inception-Net [41], and a mini-Res Net [42]. |
| Dataset Splits | No | The paper mentions 'm = 5000 training samples' and '5000 test samples', but does not specify a separate validation set split or its size. |
| Hardware Specification | No | The paper mentions that it was implemented in 'Tensorflow platform', but it does not specify any hardware details like CPU, GPU models, or memory. |
| Software Dependencies | No | The paper mentions 'Tensorflow platform [37]', but it does not provide a specific version number for Tensorflow or any other software dependencies. |
| Experiment Setup | Yes | In the experiments, we simulated a federated learning scenario with n = 10 nodes where each node observes m = 5000 training samples. We manipulated the training samples at each node via an affine distribution shift randomly generated according to a Gaussian distribution. We considered three standard neural network architectures in the literature: Alex Net [40], Inception-Net [41], and a mini-Res Net [42]. We tested the performance of the neural net classifiers trained by Fed Robust, Fed Avg, distributed FGM, and distributed PGD under different levels of affine distribution shifts. Figure 1 shows the accuracy performance over CIFAR-10 with Alex Net, Inception-Net, and Res Net architectures. We performed additional numerical experiments to analyze the effect of network size n and minimization iteration count on the robustness performance. Figure 3 shows the results of our experiments for a larger network size of n = 100 Alex Net neural network classifiers, each trained using m = 500 MNIST training data points. To examine the effect of parameter , i.e., minimization step count per training iteration, on our experimental results, we performed the CIFAR-10 experiment with the Alex Net architecture for = 5 as demonstrated in Figure 4. |