feat(baselines) Add Flanders baseline

baselines/flanders/.gitignore

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+outputs/*
+clients_params/*
+flanders/datasets_files/*
+*.log
+flanders/__pycache__
+MNIST
+.DS_Store
+*/__pycache__
+multirun

baselines/flanders/LICENSE

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baselines/flanders/README.md

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+---
+title: Protecting Federated Learning from Extreme Model Poisoning Attacks via Multidimensional Time Series Anomaly Detection
+url: https://arxiv.org/abs/2303.16668
+labels: [robustness, model poisoning, anomaly detection, autoregressive model, regression, classification]
+dataset: [MNIST, FashionMNIST]
+---
+
+**Paper:** [arxiv.org/abs/2303.16668](https://arxiv.org/abs/2303.16668)
+
+**Authors:** Edoardo Gabrielli, Gabriele Tolomei, Dimitri Belli, Vittorio Miori
+
+**Abstract:** Current defense mechanisms against model poisoning attacks in federated learning (FL) systems have proven effective up to a certain threshold of malicious clients. In this work, we introduce FLANDERS, a novel pre-aggregation filter for FL resilient to large-scale model poisoning attacks, i.e., when malicious clients far exceed legitimate participants. FLANDERS treats the sequence of local models sent by clients in each FL round as a matrix-valued time series. Then, it identifies malicious client updates as outliers in this time series by comparing actual observations with estimates generated by a matrix autoregressive forecasting model maintained by the server. Experiments conducted in several non-iid FL setups show that FLANDERS significantly improves robustness across a wide spectrum of attacks when paired with standard and robust existing aggregation methods. 
+
+## About this baseline
+
+**What’s implemented:** The code in this directory replicates the results of FLANDERS+\[baseline\] on MNIST and Fashion-MNIST under all attack settings: Gaussian, LIE, OPT, and AGR-MM; with $r=[0.2,0.6,0.8]$ (i.e., the fraction of malicious clients), specifically about tables 1, 3, 10, 11, 15, 17, 19, 20 and Figure 3.
+
+**Datasets:** MNIST, FMNIST
+
+**Hardware Setup:** AMD Ryzen 9, 64 GB RAM, and an NVIDIA 4090 GPU with 24 GB VRAM.
+
+**Estimated time to run:** You can expect to run experiments on the given setup in 2m with *MNIST* and 3m with *Fashion-MNIST*, without attacks. With an Apple M2 Pro, 16gb RAM, each experiment with 10 clients for MNIST runs in about 24 minutes. Note that experiments with OPT (fang) and AGR-MM (minmax) can be up to 5x times slower.
+
+**Contributors:** Edoardo Gabrielli, Sapienza University of Rome ([GitHub](https://github.com/edogab33), [Scholar](https://scholar.google.com/citations?user=b3bePdYAAAAJ))
+
+
+## Experimental Setup
+
+Please, checkout Appendix F and G of the paper for a comprehensive overview of the hyperparameters setup, however here's a summary.
+
+**Task:** Image classification
+
+**Models:**
+
+MNIST (multilabel classification, fully connected, feed forward NN):
+- Multilevel Perceptron (MLP)
+- minimizing multiclass cross-entropy loss using Adam optimizer
+- input: 784
+- hidden layer 1: 128
+- hidden layer 2: 256
+
+Fashion-MNIST (multilabel classification, fully connected, feed forward NN):
+- Multilevel Perceptron (MLP)
+- minimizing multiclass cross-entropy loss using Adam optimizer
+- input: 784
+- hidden layer 1: 256
+- hidden layer 2: 128
+- hidden layer 3: 64
+
+**Dataset:** Every dataset is partitioned into two disjoint sets: 80% for training and 20% for testing. The training set is distributed across all clients (100) by using the Dirichlet distribution with $\alpha=0.5$, simulating a high non-i.i.d. scenario, while the testing set is uniform and held by the server to evaluate the global model.
+
+| Description | Default Value |
+| ----------- | ----- |
+| Partitions | 100 |
+| Evaluation | centralized |
+| Training set | 80% |
+| Testing set | 20% |
+| Distribution | Dirichlet |
+| $\alpha$ | 0.5 |
+
+**Training Hyperparameters:**
+
+| Dataset | # of clients  | Clients per round | # of rounds | Batch size | Learning rate | Optimizer | Dropout | Alpha | Beta | # of clients to keep | Sampling |
+| -- | -- | -- | -- | -- | -- | -- | -- | -- | -- | -- | -- |
+| MNIST | 100 | 100 | 50 | 32 | $10^{-3}$ | Adam | 0.2 | 0.0 | 0.0 | $m - b$ | 500 |
+| FMNIST | 100 | 100 | 50 | 32 | $10^{-3}$ | Adam | 0.2 | 0.0 | 0.0 | $m - b$ | 500 |
+
+Where $m$ is the number of clients partecipating during n-th round and $b$ is the number of malicious clients. The variable $sampling$ identifies how many parameters MAR analyzes.
+
+
+## Environment Setup
+
+```bash  
+# Use a version of Python >=3.9 and <3.12.0.
+pyenv local 3.10.12
+poetry env use 3.10.12
+
+# Install everything from the toml
+poetry install
+
+# Activate the env
+poetry shell
+```
+
+
+## Running the Experiments
+Ensure that the environment is properly set up, then run:
+
+```bash  
+python -m flanders.main
+```
+
+To execute a single experiment with the default values in `conf/base.yaml`.
+
+To run custom experiments, you can override the default values like that:
+
+```bash
+python -m flanders.main dataset=income server.attack_fn=lie server.num_malicious=1
+```
+
+To run multiple custom experiments:
+
+```bash
+python -m flanders.main --multirun dataset=mnist,fmnist server.attack_fn=gaussian,lie,fang,minmax server.num_malicious=0,1,2,3,4,5
+```
+
+Finally, to run all the experiments of the paper, I've set up a script:
+
+```bash
+sh run.sh
+```
+
+## Expected Results
+
+Use this command to run experiments on FLANDERS+\[baseline\]
+```bash
+python -m flanders.main --multirun server.num_rounds=50 dataset=fmnist strategy=flanders aggregate_fn=fedavg,trimmedmean,fedmedian,krum,bulyan server.pool_size=100 server.num_malicious=0,20,60,80 server.attack_fn=gaussian,lie,fang,minmax client_resources.num_cpus=0.1 client_resources.num_gpus=0.1
+```
+
+And then this command to run experiments on vanilla baselines:
+
+```bash
+python -m flanders.main --multirun server.num_rounds=50 dataset=fmnist strategy=fedavg,trimmedmean,fedmedian,krum,bulyan server.pool_size=100 server.num_malicious=0,20,60,80 server.attack_fn=gaussian,lie,fang,minmax client_resources.num_cpus=0.1 client_resources.num_gpus=0.1
+```
+
+They will generate the results in `results/all_results.csv`.
+
+To generate the plots, including the tables shown below, use the notebook in `plotting/`.
+
+
+### Accuracy over multiple rounds
+**(left) MNIST, FLANDERS+FedAvg with 80% of malicious clients (b = 80); (right) Vanilla FedAvg in the same setting:**
+
+![acc_over_rounds](_static/screenshot-8.png)
+
+### Precision and Recall of FLANDERS
+
+**b = 20:**
+
+![alt text](_static/screenshot-4.png)
+---
+
+**b = 60:**
+
+![alt text](_static/screenshot-5.png)
+---
+**b = 80:**
+
+![alt text](_static/screenshot-6.png)
+
+
+### Accuracy w.r.t. number of attackers:
+**b = 0:**
+
+![alt text](_static/screenshot.png)
+
+---
+**b = 20:**
+
+![alt text](_static/screenshot-1.png)
+
+---
+**b = 60:**
+
+![alt text](_static/screenshot-2.png)
+
+---
+**b = 80:**
+
+![alt text](_static/screenshot-3.png)

baselines/flanders/flanders/__init__.py

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+"""FLANDERS package."""